Survey marker, image processing apparatus, image processing method, and program

ABSTRACT

There is provided a survey marker, and an image processing apparatus, method, and program capable of accurately detecting a survey marker from a captured image obtained by image capturing. The survey marker has a planar shape and includes a plurality of circles concentrically disposed, the plurality of circles including adjacent circles each having a different color. A candidate region extraction unit extracts a candidate region from a captured image obtained by image capturing of the survey marker, the candidate region being a candidate of a region in which the survey marker appears. A feature amount extraction unit extracts a feature amount of the candidate region. A discrimination unit discriminates the survey marker on the basis of the feature amount. The present technology can be applied to, for example, a case of detecting a survey marker installed on the ground from a captured image obtained by aerial image capturing.

TECHNICAL FIELD

The present technology relates to a survey marker, an image processingapparatus, an image processing method, and a program, and particularlyto, a survey marker, an image processing apparatus, an image processingmethod, and a program that are capable of, for example, accuratelydetecting a survey marker from a captured image obtained by imagecapturing of the survey marker.

BACKGROUND ART

For example, the technology of installing and capturing an image of asurvey marker, and creating a three-dimensional model on the basis of acontrol point at which the survey marker appearing in the captured imageobtained by the image capturing is installed, thus easily performingmeasurement of a building or the like within a real space has beenproposed (see, e.g., Patent Literature 1).

CITATION LIST Patent Literature

Patent Literature 1: Japanese Patent Application Laid-open No.2005-140550

DISCLOSURE OF INVENTION Technical Problem

When an image of a survey marker is captured and measurement of abuilding, a soil volume, or the like is performed by using the capturedimage obtained by image capturing of the survey marker, accuratelydetecting the survey marker from the captured image is requested.

The present technology has been made in view of the circumstances asdescribed above and enables a survey marker to be accurately detectedfrom a captured image obtained by image capturing of the survey marker.

Solution to Problem

An image processing apparatus or a program of the present technology isan image processing apparatus including: a candidate region extractionunit that extracts a candidate region from a captured image obtained byimage capturing of a survey marker, the candidate region being acandidate of a region in which the survey marker appears, the surveymarker having a planar shape and including a plurality of circlesconcentrically disposed, the plurality of circles including adjacentcircles each having a different luminance or hue; a feature amountextraction unit that extracts a feature amount of the candidate region;and a discrimination unit that discriminates the survey marker on thebasis of the feature amount, or a program for causing a computer tofunction as the image processing apparatus as described above.

An image processing method of the present technology is an imageprocessing method including: extracting a candidate region from acaptured image obtained by image capturing of a survey marker, thecandidate region being a candidate of a region in which the surveymarker appears, the survey marker having a planar shape and including aplurality of circles concentrically disposed, the plurality of circlesincluding adjacent circles each having a different luminance or hue;extracting a feature amount of the candidate region; and discriminatingthe survey marker on the basis of the feature amount.

In the image processing apparatus, the image processing method, and theprogram of the present technology, a candidate region is extracted froma captured image obtained by image capturing of a survey marker, thecandidate region being a candidate of a region in which the surveymarker appears, the survey marker having a planar shape and including aplurality of circles concentrically disposed, the plurality of circlesincluding adjacent circles each having a different luminance or hue. Afeature amount of the candidate region is extracted. The survey markeris then discriminated on the basis of the feature amount.

The survey marker of the present technology is a survey marker having aplanar shape and including a plurality of circles concentricallydisposed and each having a different radius, the plurality of circlesincluding adjacent circles each having a different luminance or hue.

In the survey marker of the present technology, the planar shape is ashape including a plurality of circles concentrically disposed and eachhaving a different radius, and the plurality of circles include adjacentcircles each having a different luminance or hue.

Note that the image processing apparatus may be an independent apparatusor an internal block forming a single apparatus.

Further, constituent elements of the image processing apparatus can bedistributed and integrated in a plurality of apparatuses.

Furthermore, the program can be provided by transmission via atransmission medium or by recording on a recording medium.

Advantageous Effects of Invention

According to the present technology, it is possible to accurately detecta survey marker from a captured image obtained by image capturing of thesurvey marker.

Note that the effects disclosed herein are not necessarily limited andmay be any effect described in the present disclosure.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a diagram for describing the general outline of an embodimentof a soil-volume measurement system to which the present technology isapplied.

FIG. 2 is a flowchart for describing an example of a workflow ofsoil-volume measurement performed in the soil-volume measurement system.

FIG. 3 is a plan view showing a first example of a survey marker 10.

FIG. 4 is a plan view showing a second example of the survey marker 10.

FIG. 5 is a plan view showing a third example of the survey markers 10.

FIG. 6 is a perspective view showing an example of a secondmulti-circle-type marker as the survey marker 10.

FIG. 7 is a diagram for describing the color of the survey marker 10.

FIG. 8 is a diagram for describing the presence/absence of occurrence ofcolor mixture between two predetermined colors.

FIG. 9 is a block diagram showing a hardware configuration example of acomputer as a cloud server 30.

FIG. 10 is a block diagram showing a functional configuration example ofthe cloud server 30 that functions as an image processing apparatus(detection apparatus).

FIG. 11 is a flowchart for describing an example of detection processingof detecting the survey marker 10.

FIG. 12 is a flowchart for describing an example of detailed processingof binarizing each pixel of a captured image.

FIG. 13 is a diagram showing an example of a template image of (circles11 and 12 of) the survey marker 10.

FIG. 14 is a diagram showing an example of filters that emphasize colorsprovided to the circles 11 and 12 in each of the candidate region andthe template image.

FIG. 15 is a flowchart for describing an example of processing ofextracting a distance DF between the hues of the respective circles 11and 12 as a feature amount.

FIG. 16 is a block diagram showing a configuration example of the drone20.

FIG. 17 is a diagram for describing the general outline of anotherembodiment of the soil-volume measurement system to which the presenttechnology is applied.

FIG. 18 is a plan view showing a first modified example of the surveymarker 10 of the multi-circle-type marker.

FIG. 19 is a perspective view showing a second modified example of thesurvey marker 10 of the multi-circle-type marker.

FIG. 20 is a perspective view showing a third modified example of thesurvey marker 10 of the multi-circle-type marker.

FIG. 21 is a perspective view showing a fourth modified example of thesurvey marker 10 of the multi-circle-type marker.

FIG. 22 is a perspective view showing a fifth modified example of thesurvey marker 10 of the multi-circle-type marker.

FIG. 23 is a perspective view showing a sixth modified example of thesurvey marker 10 of the multi-circle-type marker.

FIG. 24 is a perspective view showing a seventh modified example of thesurvey marker 10 of the multi-circle-type marker.

FIG. 25 is a perspective view showing an eighth modified example of thesurvey marker 10 of the multi-circle-type marker.

FIG. 26 is a perspective view showing a ninth modified example of thesurvey marker 10 of the multi-circle-type marker.

FIG. 27 is a perspective view showing a tenth modified example of thesurvey marker 10 of the multi-circle-type marker.

FIG. 28 is a perspective view showing a eleventh modified example of thesurvey marker 10 of the multi-circle-type marker.

FIG. 29 is a plan view showing a twelfth modified example of the surveymarker 10 of the multi-circle-type marker.

FIG. 30 is a perspective view showing a thirteenth modified example ofthe survey marker 10 of the multi-circle-type marker.

FIG. 31 is a diagram showing an HLS color space.

FIG. 32 is a diagram for describing the general outline of the detectionof the survey marker 10 by using luminance.

FIG. 33 is a diagram for describing the general outline of the detectionof the survey marker 10 by using the luminance in a case where thesurvey marker 10 includes the circles 11 to 13.

FIG. 34 is a flowchart for describing another example of the detectionprocessing.

FIG. 35 is a flowchart for describing an example of detailed processingof binarizing each pixel of a captured image, which is performed in StepS131-1.

FIG. 36 is a flowchart for describing an example of processing ofextracting a distance between the luminances of the respective circles11 and 12 as a feature amount in feature amount extraction processingperformed in Step S132.

MODE(S) FOR CARRYING OUT THE INVENTION

<Embodiment of Soil-Volume Measurement System to Which PresentTechnology is Applied>

FIG. 1 is a diagram for describing the general outline of an embodimentof a soil-volume measurement system to which the present technology isapplied.

In the soil-volume measurement system of FIG. 1, soil-volume measurementusing a UAV (Unmanned Aerial Vehicle) is performed.

In FIG. 1, survey markers 10 are installed on the ground. The surveymarkers 10 can be manually installed or can be installed by beingdistributed from an unmanned aircraft such as a drone or a flightvehicle such as an aircraft operated by a human. Furthermore, the surveymarker 10 itself may be moved by installing the survey marker 10 on theback of the drone.

Images of the survey markers 10 are aerially captured. In FIG. 1, adrone 20 equipped with a camera 21 is caused to fly to capture images ofthe survey markers 10 (aerially capture images of the survey markers 10)with the camera 21 mounted to the drone 20.

Captured images obtained by image capturing of the survey markers 10with the camera 21 (e.g., still images) are transmitted to, for example,a cloud server 30 by wireless communication or wired communication.

The cloud server 30 performs image processing on the captured imagesfrom the camera 21 and thus detects the survey markers 10 appearing inthe captured images. Furthermore, the cloud server 30 creates athree-dimensional model of the land form of the ground by using adetection result of the survey markers 10, performs soil-volumemeasurement of the land form or the like of the ground from thethree-dimensional model, and outputs a measurement result of thesoil-volume measurement.

Note that the processing performed by the cloud server 30 describedabove can be performed by the drone 20, not by the cloud server 30.Further, the processing performed by the cloud server 30 described abovecan be shared between the drone 20 and the cloud server 30.

Furthermore, the method of aerially capturing images of the surveymarkers 10 is not limited to the method using the drone 20. In otherwords, the aerial image capturing of the survey markers 10 can beperformed by, in addition to the method using an unmanned vehicle suchas the drone 20, using a flight vehicle in which a human rides to driveit or an artificial satellite, for example.

Further, for the survey marker 10, paper, plastic, or the like on whicha predetermined graphic is printed can be employed. Further, for thesurvey marker 10, a laminate of plate-like materials of plastic, rubber,or the like having a predetermined shape can be employed. Furthermore,for the survey marker 10, a display panel such as an LCD (Liquid CrystalDisplay) or an organic EL (Electro Luminescence) display, which displaysa predetermined graphic, can be employed. Additionally, for the surveymarker 10, a member such as a reflector board, which is spread andunfolded, can also be employed.

FIG. 2 is a flowchart for describing an example of a workflow of thesoil-volume measurement performed in the soil-volume measurement systemof FIG. 1.

In Step S11, for example, a manager who performs the soil-volumemeasurement makes advance planning of the soil-volume measurement. Inthe advance planning, a determination on a flight route of the drone 20,a determination on (a position to be) a control point at which thesurvey marker 10 is to be installed, or the like is performed.

In Step S12, according to the advance planning, the survey markers 10are installed at control points that are set with intervals of severalhundreds of meters, for example. The installation of the survey markers10 can be performed manually or with a movable robot, for example.Furthermore, the survey marker 10 itself may be a movable robot.

In Step S13, a horizontal position (latitude and longitude) and analtitude of the control point at which each survey marker 10 isinstalled are measured.

In Step S14, according to the advance planning, the drone 20 is causedto fly to aerially capture images of the survey markers 10, that is, tocapture an image of the ground on which the survey markers 10 areinstalled (predetermined land surface range as soil-volume measurementtarget), with the camera 21 mounted to the drone 20.

In the aerial image capturing of the survey markers 10, one or morecaptured images are captured as captured image data. Furthermore, theaerial image capturing of the survey markers 10 is performed such that,when image capturing ranges that appear in all of the captured imagesare gathered, the whole range where the survey markers 10 are installedappears in the gathering of the image capturing ranges.

Further, the aerial image capturing of the survey markers 10 isperformed such that an image capturing range appearing in a certaincaptured image and an image capturing range appearing in anothercaptured image partially overlap with each other.

In Step S15, the survey markers 10 installed on the ground areretrieved, and the captured image data obtained by image capturing ofthe survey markers 10 with the camera 21 is uploaded (transmitted) tothe cloud server 30.

In Step S16, the cloud server 30 performs detection processing ofdetecting, from the captured images captured with the camera 21, (theimages of) the survey markers 10 appearing in the captured images.

In Step S17, the cloud server 30 performs processing of generatingthree-dimensional model data of the ground by using the horizontalposition and the altitude of the control point, which are measured inStep S13, and the detection result data of the survey markers 10, whichis obtained in the detection processing performed in Step S16.

In Step S18, the cloud server 30 performs soil-volume measurementprocessing by using the three-dimensional model data of the ground andperforms processing of outputting measurement result data of thesoil-volume measurement.

<Survey Marker 10>

FIG. 3 is a plan view showing a first example of the survey markers 10.

The survey markers 10 of FIG. 3 are survey markers called a star type,an X type, and a plus (+) type.

In the survey markers 10 of the star type, the X type, and the plustype, a white color and a black color that do not have hues are given totwo regions adjacent to each other.

Here, in the aerial image capturing of the survey markers 10 installedon the ground, if the image capturing of the survey markers 10 isperformed from a position as high as possible, an image of a wider rangecan be captured, and the number of captured images can be reduced.

When the number of captured images is reduced, it is possible to reducean overlapping range of an image capturing range appearing in a certaincaptured image and an image capturing range appearing in anothercaptured image, a time during which the captured image is uploaded tothe cloud server 30, the load caused when the cloud server 30 processesthe captured images, and the like.

However, (the images of) the survey markers 10 appearing in the capturedimages become small when the images of the survey markers 10 arecaptured from a height.

Furthermore, in a case where the survey marker 10 is a marker having awhite color and a black color, such as the survey marker of the startype, the X type, or the plus type, there is a possibility that theexpansion of the white color, the contraction of the black color, andthe like are caused in the captured image, and similar patterns arecaused by black of the soil of the ground and white of snow due to snowcoverage. Accordingly, the detection accuracy for detecting the surveymarker 10 from the captured image may be lowered.

Further, in the survey marker 10 of the star type, the X type, or theplus type, an intersection point of (the extended lines of) the boundarylines between (the regions provided with) the white color and the blackcolor is detected as the center of the survey marker 10. Therefore, whenthe expansion of the white color and the contraction of the black coloroccur, the detection accuracy for detecting the center of the surveymarker 10 may be lowered.

FIG. 4 is a plan view showing a second example of the survey marker 10.

In the survey marker 10 of FIG. 4, a circle of a black color is disposedwithin a rectangle of a white color.

Since the survey marker 10 of FIG. 4 has a simple configuration ascompared to the survey markers 10 of FIG. 3, an object that appears likea black circle in the captured image may be erroneously detected as asurvey marker 10.

Here, since the survey marker 10 of FIG. 4 has a single circle, it canbe called a single-circle-type marker.

FIG. 5 is a plan view showing a third example of the survey markers 10.

The survey markers 10 of FIG. 5 are markers, each of which has a planarshape and includes a plurality of circles concentrically disposed andeach having a different radius, and the circles adjacent to each otheramong the plurality of circles each have a different hue.

Here, the planar shape means the shape of an object, which is depictedin a plan view when the object is expressed in the plan view.

The survey markers 10 of FIG. 5 each have a plurality of circles, andcan thus be called multi-circle-type markers.

According to the survey markers 10 of the multi-circle-type markers (thesame holds true for the single-circle-type marker of FIG. 4), it ispossible to detect the survey marker 10 without considering theorientation (rotation) of the survey marker 10 appearing in the capturedimage, and to mitigate the load of the cloud server 30 for the detectionprocessing of detecting the survey marker 10. Furthermore, it ispossible to easily detect the center of the survey marker 10.

A of FIG. 5 is a plan view showing an example of a firstmulti-circle-type marker as the survey marker 10.

The survey marker 10 in A of FIG. 5 has a planar shape in which threecircles 11, 12, and 13 each having a different radius are concentricallydisposed, and a rectangular frame region 14 having a shape such as asquare or a rectangle and including the three circles 11 to 13 isdisposed.

In FIG. 5, the radius increases in the order of the circles 11 to 13.

Furthermore, in FIG. 5, among the circles 11 to 13, adjacent circleshave different hues.

In other words, in FIG. 5, the color of the circle 11 having thesmallest radius is, for example, a blue color that is one of chromaticcolors (having hues), and the color of the circle 12 having the secondsmallest radius is, for example, a red color that is another one of thechromatic colors. Furthermore, the color of the circle 13 having thethird smallest (the largest) radius is, for example, a black color thatis one of achromatic colors.

Note that in the multi-circle-type marker as the survey marker 10, theadjacent circles only need to have different hues. Therefore, if theadjacent circles 11 and 12 have different hues and the adjacent circles12 and 13 have different hues, the circles 11 and 13 that are notadjacent to each other may have the same hue.

In other words, for example, the black color of the achromatic color,the red color of the chromatic color, and the black color of theachromatic color can be employed as the colors of the circles 11 to 13,respectively.

The frame region 14 can be made of, for example, rectangular paper orplastic or the like.

In a case where the frame region 14 is made of rectangular paper orplastic or the like, the survey marker 10 can be constituted by, forexample, printing the circles 11 to 13 in the frame region 14 made ofpaper, plastic, or the like.

Further, the circles 11 to 13 and the frame region 14 can be made of,for example, plate-like materials such as plastic, rubber, or the like.In this case, the survey marker 10 can be constituted by superimposingthe plate-like materials, which are the circles 11 to 13 and the frameregion 14, in the order of the frame region 14 and the circles 13, 12,and 11 from the bottom to the top.

In addition, the survey marker 10 can include, for example, a displaypanel such as an LCD or an organic EL display. When the display panel iscaused to display the circles 11 to 13 and the frame region 14, thedisplay panel can be caused to function as the survey marker 10.

Note that the date of the installation of the survey marker 10 and othercomments can be described in a region, of the frame region 14, in whichthe circles 11 to 13 are excluded.

B of FIG. 5 is a plan view showing an example of a secondmulti-circle-type marker as the survey marker 10.

The survey marker 10 in B of FIG. 5 has a configuration in which theframe region 14 is not provided as compared to the multi-circle-typemarker in A of FIG. 5. Therefore, the survey marker 10 in B of FIG. 5has a configuration in which the three circles 11, 12, and 13 eachhaving a different radius are concentrically disposed.

C of FIG. 5 is a plan view showing an example of a thirdmulti-circle-type marker as the survey marker 10.

The survey marker 10 in C of FIG. 5 has a configuration in which thecircle 13 and the frame region 14 are not provided as compared to themulti-circle-type marker in A of FIG. 5. Therefore, the survey marker 10in C of FIG. 5 has a configuration in which the two circles 11 and 12each having a different radius are concentrically disposed.

D of FIG. 5 is a plan view showing an example of a fourthmulti-circle-type marker as the survey marker 10.

The survey marker 10 in D of FIG. 5 has a configuration in which thecircle 11 and the frame region 14 are not provided as compared to themulti-circle-type marker in A of FIG. 5. Therefore, the survey marker 10in D of FIG. 5 has a configuration in which the two circles 12 and 13each having a different radius are concentrically disposed.

Note that, as the survey marker 10, other configurations such as aconfiguration in which the frame region 14 is provided to themulti-circle-type marker in C or D of FIG. 5 and a configuration inwhich four or more circles each having a different radius areconcentrically disposed can be employed.

FIG. 6 is a perspective view showing an example of the secondmulti-circle-type marker as the survey marker 10 in B of FIG. 5.

Here, the second multi-circle-type marker has a planar shape includingthe there circles 11 to 13 and are thus also called a three-circle-typemarker.

The three-circle-type marker as the survey marker 10 in FIG. 6 includesa short columnar member to be the circle 11 (hereinafter, also referredto as columnar member 11), a plate-like circular member to be the circle12 (hereinafter, also referred to as circular member 12), and aplate-like circular member to be the circle 13 (hereinafter, alsoreferred to as circular member 13).

In other words, the survey marker 10 of FIG. 6 is constituted bysuperimposing the columnar member 11 and the circular members 12 and 13in the order of the circular members 13 and 12 and the columnar member11 from the bottom to the top.

The columnar member 11 can be made of, for example, plastic (ABS resin).Furthermore, the columnar member 11 can be configured to be hollow andcan integrate an illuminance detection apparatus including anilluminance sensor that detects the illuminance of (on) the surveymarker 10, a communication apparatus including an antenna and a circuitthat perform wireless communication, a recording apparatus including arecording medium such as a semiconductor on which information detectedby the illuminance detection apparatus is recorded in chronologicalorder, and the like (which are not shown). Note that the survey marker10 may integrate those illuminance detection apparatus and others in apart of the survey marker, the part excluding the columnar member 11.Further, the survey marker 10 may integrate another sensor other thanthe illuminance sensor in the columnar member 11 or another member andmay transmit, by the communication apparatus, data regarding the surveymarker detected by the sensor or may record the data by the recordingapparatus.

For example, in a case where the columnar member 11 is caused tointegrate the illuminance detection apparatus and the communicationapparatus, in the survey marker 10, information of the illuminancedetected by the illuminance detection apparatus can be transmitted bythe communication apparatus.

The information of the illuminance or the like transmitted from thesurvey marker 10 can be received in the cloud server 30 to be put to usein the processing in the cloud server 30.

Note that in a case where the illuminance detection apparatus, thecommunication apparatus, or the like is not integrated in the columnarmember 11, the columnar member 11 can be constituted to be plate-likeand circular as in the circular member 12 or 13. Further, an apparatusto be integrated in the columnar member 11 can be taken out (detached)from the columnar member 11 for the purpose of charging or the like.

The circular member 12 can be made of, for example, a member such asrubber, which is difficult to discolor by ultraviolet rays. When thecircular member 12 is made of a member difficult to discolor byultraviolet rays, and when the hue of the color provided to the circularmember 12 is used to detect the survey marker 10, lowering of thedetection accuracy of the survey marker 10 due to the discoloring of thecircular member 12 can be suppressed.

The circular member 13 can be made of, for example, an insulator ofpolypropylene or the like. When the circular member 13 is made of theinsulator, the circular member 12, the columnar member 11, and furtherthe communication apparatus or the like integrated in the columnarmember 11 can be prevented from being electrically connected to theground (earth).

Here, as will be described later, in order (to extract a candidateregion to be a candidate of the survey marker 10 from the capturedimage) to recognize the survey marker 10, at least a hue of the color ofthe circular member 12 is used.

In a case where the survey marker 10 is constituted without includingthe circular member 13, when the survey marker 10 is installed on theground, the circular member 12 comes into contact with the ground.Various colors may exist as a color of the installation site of thesurvey marker 10. Thus, depending on the color of the installation siteof the survey marker 10, a large degree of color mixture occurs betweenthe color of the circular member 12 and the color of the installationsite of the survey marker 10 in the captured image, and discriminationof the survey marker 10 is affected according to the degree of the colormixture.

In this regard, when the survey marker 10 is provided with the circularmember 13, the color mixture between the color of the circular member 12and the color of the installation site of the survey marker 10 can beprevented from occurring.

Note that, in this case, the color mixture between the colors of thecircular members 12 and 13 affects the discrimination of the surveymarker 10.

However, in a case where the survey marker 10 is constituted without thecircular member 13, the degree of the color mixture between the color ofthe circular member 12 and the color of the installation site of thesurvey marker 10 varies depending on the color of the installation siteof the survey marker 10. Therefore, the degree at which the colormixture between the color of the circular member 12 and the color of theinstallation site of the survey marker 10 affects the discrimination ofthe survey marker 10 varies depending on the color of the installationsite of the survey marker 10.

In contrast to this, in a case where the survey marker 10 is providedwith the circular member 13, the degree of the color mixture between thecolors of the respective circular members 12 and 13 does not varydepending on the color of the installation site of the survey marker 10.Therefore, the degree at which the color mixture between the colors ofthe respective circular members 12 and 13 affects the discrimination ofthe survey marker 10 does not vary depending on the color of theinstallation site of the survey marker 10.

As described above, with the circular member 13, it is possible toprevent the degree of the color mixture with the color of the circularmember 12 from varying depending on the color of the installation siteof the survey marker 10.

Here, for the size of the multi-circle-type marker as the survey marker10, for example, a size having a diameter of approximately 30 cm, suchas a 10 to 30-cm square, can be employed such that a human can carry acertain number of survey markers 10 in consideration of carrying of thesurvey markers 10 when the human installs the survey markers 10.

FIG. 7 is a diagram for describing the color of the survey marker 10.

Here, as described in FIGS. 1 and 2, the cloud server 30 detects, from acaptured image obtained by aerial image capturing, the survey marker 10appearing in the captured image.

The cloud server 30 detects the survey marker 10 by using, for example,the hue of the circle (circular member) 12. In other words, the cloudserver 30 detects the survey marker 10 by using, for example, the hueitself of the circle 12, a distance between the hue of the circle 12 andthe hue of the circle 11 adjacent to the circle 12, or the like.

When attention is focused on the use of the hue itself of the circle 12or a distance between the hues of the respective circles 11 and 12adjacent to each other in order to detect the survey marker 10, it iseffective that the colors of the respective circles 11 and 12 are colorsdifficult to cause the color mixture (colors causing low degree of colormixture) when image capturing is performed from a certain height, i.e.,for example, a height at which the aerial image capturing is scheduled.

According to the experiment performed by the inventors of the subjectapplication, the suppression of the color mixture is confirmed, forexample, in a case where a combination of the black color for the colorof the circle 11 and a color having a hue different from the blackcolor, which is for the color of the circle 12, is employed as the colorcombination of the circle 11 and the circle 12.

For example, in a case where a combination of the black color for thecolor of the circle 11 and a red color for the color of the circle 12 isemployed as the color combination of the circle 11 and the circle 12, itis confirmed that the color mixture remains to an extent that the blackcolor of the circle 11 and the red color of the circle 12 are visible ina captured image obtained by aerial image capturing from the height of65 m.

Note that when the circle 11 is too large as compared to the circle 12,the saturation of the circle 12 appearing in the captured image isreduced, which makes it difficult to discriminate the circle 12.Meanwhile, when the circle 11 is too small as compared to the circle 12,the lightness of the circle 12 appearing in the captured image isreduced, which makes it difficult to discriminate the circle 12.

In this regard, it is effective that the circles 11 and 12 are set tohave the sizes at which the ease of discrimination of the circle 12 isincreased.

According to the experiment performed by the inventors of the subjectapplication, it is confirmed that when an area of a portion of thecircle 12, the portion excluding the circle 11, is set to approximatelysubstantially 1.0 to 3.0 times the area of the circle 11, the ease ofdiscrimination of the circle 12 is increased.

When attention is focused on the use of the distance between the hues ofthe respective circles 11 and 12 adjacent to each other in order todetect the survey marker 10, it is effective that the combination of thecolors of the respective circles 11 and 12 is a combination whosepossibility of existing in nature is as low as possible.

Furthermore, it is effective that the combination of the colors of therespective circles 11 and 12 is a combination in which the hues of therespective colors are as different as possible.

Further, it is effective that the combination of the colors of therespective circles 11 and 12 is a combination in which the degree of thecolor mixture is as low as possible when image capturing is performedfrom a certain height, i.e., for example, a combination in which adistance between the hue of the circle 11 and the hue of the circle 12,which are obtained from the captured image, is as large as possible.

FIG. 7 shows an example of a histogram of the hues of pixels of therespective circles 11 and 12, which are obtained from the captured imageobtained by image capturing of the survey marker 10.

Here, unless otherwise stated, the circle 12 means an annular portionthat excludes the circle 11 in the entire circle as the circle 12.

In the histogram of FIG. 7, the pixels of (the region estimated as) thecircle 11 and (the pixels estimated as) the circle 12 are detected fromthe captured image, and for the pixels of those circles 11 and 12, thefrequency of the pixels (the number of pixels) having the respectivehues is shown.

Note that in FIG. 7 the horizontal axis represents the hue and thevertical axis represents the frequency.

In the hue histogram for the pixels of the circles 11 and 12 detectedfrom the captured image (hereinafter, also referred to as huehistogram), for example, as shown in FIG. 7, two distributions, adistribution with a peak of a first hue and a distribution with a peakof a second hue are present.

For the distance between the hues of the respective circles 11 and 12,for example, a distance between the peaks of the two respectivedistributions (difference in hue between peaks) existing in the huehistogram can be employed.

Further, for the distance between the hues of the respective circles 11and 12, for example, a difference in integrated value such as a meanvalue of the hues of the respective pixels between the respectivecircles 11 and 12 detected from the captured image can be employed.

Now, for example, assuming that a difference in mean value of the huesof the respective pixels between the circles 11 and 12 detected from thecaptured image is employed as a distance DF between the hues of therespective circles 11 and 12, the distance DF between the hues of therespective circles 11 and 12 is expressed by Expression (1).

$\begin{matrix}\left\lbrack {{Math}.\mspace{14mu} 1} \right\rbrack & \; \\{{DF} = {{\frac{\sum\limits_{{({i,j})} \in {{Area}\; 1}}\; H_{i,j}}{N\; 1} - \frac{\sum\limits_{{({i,j})} \in {{Area}\; 2}}\; H_{i,j}}{N\; 2}}}} & (1)\end{matrix}$

In Expression (1), H_(i, j) represents the hue of the pixel at aposition (i, j) of the captured image. N1 and N2 represent the number ofpixels of the circles 11 and 12 detected from the captured image,respectively. The summation (E) of the first term on the right siderepresents the summation for the pixel of the circle 11 detected fromthe captured image ((i, j)ϵpixels of Area1), and the summation (E) ofthe second term on the right side represents the summation for the pixelof the circle 12 detected from the captured image ((i, j)ϵpixels ofArea2).

Note that in a case where the pixel value of the pixel of the capturedimage is expressed by an R (Red) value, a G (Green) value, and a B(Blue) value of an RGB color space, the R value, the G value, and the Bvalue can be converted into a hue H (Hue), a saturation S (Saturation),and a luminance L (Lightness) of an HLS space according to Expression(2).

$\begin{matrix}\left\lbrack {{Math}.\mspace{14mu} 2} \right\rbrack & \; \\{{M = {\max \mspace{14mu} \left( {R,G,B} \right)}}{m = {\min \mspace{14mu} \left( {R,G,B} \right)}}{H = \left\{ {{\begin{matrix}{{60 \times \frac{G - R}{M - m}} + 60} & \left( {m = B} \right) \\{{60 \times \frac{B - G}{M - m}} + 180} & \left( {m = R} \right) \\{{60 \times \frac{R - B}{M - m}} + 300} & \left( {m = G} \right)\end{matrix}L} = {{\frac{M + m}{2}S} = {M - m}}} \right.}} & (2)\end{matrix}$

In Expression (2), max(A, B, C) represents the maximum value among A, B,and C, and min(A, B, C) represents the minimum value among A, B, and C.As shown in Expression (2), regarding the hue H, a conversion equationfrom RGB differs depending on whether the minimum value m is the Rvalue, the G value, or the B value.

The distance DF between the hues of the respective circles 11 and 12expresses the degree of the color mixture between the colors of therespective circles 11 and 12. As the distance DF becomes larger, thedegree of the color mixture becomes smaller.

In this regard, two predetermined colors, with which the distance DFbetween the hues has a predetermined threshold TH or more, are assumedas colors difficult to cause the color mixture (colors causing lowdegree of color mixture) and can be employed as the colors of therespective circles 11 and 12.

Hereinafter, for ease of explanation, the two colors, with which thedistance DF between the hues has a predetermined threshold TH or more,are also referred to as colors not causing the color mixture, and thetwo colors, with which the distance DF between the hues does not have apredetermined threshold TH or more, are also referred to as colorscausing the color mixture.

FIG. 8 is a diagram for describing the presence/absence of theoccurrence of the color mixture between the two predetermined colors.

Note that, for the distance DF between the hues, for example, thedifference in mean value of the hue in Expression (1) (absolutedifference value) is employed.

As shown in FIG. 8, an image of a marker including two regions adjacentto each other, to which one color c1 and the other color c2 in twopredetermined colors c1 and c2 having different hues are provided, andhaving, for example, the size nearly equal to that of the survey marker10, is captured with a camera, and a captured image in which the markerappears is obtained. The image capturing of the captured image in whichthe marker appears can be performed, for example, at a distance nearlyequal to that when the aerial image capturing of the survey marker 10 isperformed.

Furthermore, (the region of) the marker is detected from the capturedimage in which the marker appears, and from that marker, a region A1(estimated to be) provided with the color c1 and a region A2 (estimatedto be) provided with the color c2 are identified.

The distance DF between the hues of the respective regions A1 and A2 isthen calculated according to Expression (1) by using the pixel values ofthe pixels of the respective regions A1 and A2.

In a case where the distance DF does not have the threshold TH or more(DF<TH), the colors c1 and c2 are seen as the two colors causing (likelyto cause) the color mixture, and those two colors can be excluded as thecolors of the circles 11 and 12.

Meanwhile, in a case where the distance DF has the threshold TH or more(DF>=TH), the colors c1 and c2 are seen as the two colors not causing(not likely to cause) the color mixture, and those two colors can beemployed as the colors of the circles 11 and 12.

As the threshold TH of the distance DF, for example, a threshold THexpressed by Expression (3) can be employed.

[Math. 3]

TH=|H ₁ −H ₂|×0.5   (3)

In Expression (3), H₁ represents a mean value of the hues of the pixelsof the region provided with the color c1 that appears in a capturedimage obtained when close-up image capturing (e.g., image capturing atshortest focal distance) is performed for the color c1 only. Similarly,H₂ represents a mean value of the hues of the pixels of the regionprovided with the color c2 that appears in a captured image obtainedwhen the close-up image capturing is performed for the color c2 only.

The mean values of the hues of the pixels of the regions provided withthe colors c1 and c2 that appear in the captured images obtained whenthe close-up image capturing is performed are expected to have, forexample, theoretical hues of the colors c1 and c2, respectively.Therefore, for H₁ and H₂, theoretical hues of the colors c1 and c2 canalso be employed.

According to the threshold TH of Expression (3), in a case where thedistance DF between the hues is 0.5 times or more the difference betweenthe hues of the colors c1 and c2, |H₁−H₂|, the colors c1 and c2 can beemployed as the colors of the circles 11 and 12.

As described above, when the two colors, with which the distance DF hasthe threshold TH or more, are employed as the colors of the circles 11and 12, it is possible to suppress lowering of the detection accuracy ofthe survey marker 10 that results from the color mixture of the colorsprovided to the adjacent circles 11 and 12, and to accurately detect thesurvey marker 10.

Note that, regarding the above description, the same holds true for thecolors of the circles 12 and 13 adjacent to each other, in addition tothe colors of the circles 11 and 12 adjacent to each other. In otherwords, when the two colors, with which the distance DF has the thresholdTH or more, are employed as the colors of the circles 11 and 12, andwhen the two colors, with which the distance DF has the threshold TH ormore, are employed also as the colors of the circles 12 and 13, thedetection accuracy of the survey marker 10 can be further improved ascompared to the case where the two colors, with which the distance DFhas the threshold TH or more, are employed as the colors of the circles11 and 12 only.

Here, while the rectangular regions are employed as the regions A1 andA2 in FIG. 8, other regions, for example, circular regions similar tothe circles 11 and 12 can be employed as the regions A1 and A2.

<Configuration Example of Cloud Server 30>

FIG. 9 is a block diagram showing a hardware configuration example of acomputer as the cloud server 30 of FIG. 1.

The cloud server 30 integrates a CPU (Central Processing Unit) 32, andan input/output interface 40 is connected to the CPU 32 via a bus 31.

When an input unit 37 is operated by a user (operator) or the like toinput a command via the input/output interface 40, the CPU 32 executes aprogram stored in a ROM (Read Only Memory) 33 according to the command.Alternatively, the CPU 32 loads the program stored in a hard disk 35 toa RAM (Random Access Memory) 34 to execute the program. Note that theCPU 32 includes one or a plurality of processing circuits.

Accordingly, the CPU 32 performs various types of processing and causesthe cloud server 30 to function as an apparatus having a predeterminedfunction. The CPU 32 then outputs processing results of the varioustypes of processing from an output unit 36 as necessary, for example,via the input/output interface 40, or transmits the processing resultsfrom the communication unit 38, and further causes the hard disk 35 torecord the processing results, for example.

Note that the input unit 37 includes a keyboard, a mouse, a microphone,and the like. Further, the output unit 36 includes an LCD, a speaker,and the like.

Further, a program to be executed by the CPU 32 can be recorded inadvance in the hard disk 35 or the ROM 33 as a recording mediumintegrated in the cloud server 30.

Alternatively, the program can be stored (recorded) on a removablerecording medium 41. Such a removable recording medium 41 can beprovided as so-called packaged software. Here, examples of the removablerecording medium 41 include a flexible disk, a CD-ROM (Compact Disc ReadOnly Memory), MO (Magneto Optical) disc, a DVD (Digital Versatile Disc),a magnetic disk, and a semiconductor memory.

Further, the program can be installed from the removable recordingmedium 41 as described above to the cloud server 30, or can bedownloaded to the cloud server 30 via a communication network or abroadcasting network and then installed in the built-in hard disk 35. Inother words, the program can be wirelessly transferred to the cloudserver 30, for example, from a download site via a satellite for digitalsatellite broadcasting or can be transferred by wire to the cloud server30 via a network such as a LAN (Local Area Network) or the Internet.

As described above, the CPU 32 executes the program and thus causes thecloud server 30 to function as an apparatus having a predeterminedfunction.

For example, the CPU 32 causes the cloud server 30 to function as animage processing apparatus that performs image processing on a capturedimage from the camera 21. In this case, the cloud server 30 as the imageprocessing apparatus performs detection processing of detecting thesurvey marker 10 appearing in the captured image. Therefore, the cloudserver 30 can also be a detection apparatus that performs such detectionprocessing.

FIG. 10 is a block diagram showing a functional configuration example ofthe cloud server 30 that functions as the image processing apparatus(detection apparatus) as described above.

In FIG. 10, the cloud server 30 includes a candidate region extractionunit 61, a feature amount extraction unit 62, and a discrimination unit63. The candidate region extraction unit 61, the feature amountextraction unit 62, and the discrimination unit 63 are constituted by,for example, the CPU 32 of FIG. 9.

The captured image from the camera is supplied to the candidate regionextraction unit 61 and the discrimination unit 63.

The candidate region extraction unit 61 extracts a candidate region,which is a candidate of a region in which (the circle 12 of) the surveymarker 10 appears, from a captured image obtained from the camera 21 byimage capturing of the survey marker 10, and supplies the candidateregion to the feature amount extraction unit 62.

The feature amount extraction unit 62 extracts a feature amount of thecandidate region from the candidate region obtained from the candidateregion extraction unit 61, and supplies the feature amount to thediscrimination unit 63.

The discrimination unit 63 discriminates (the region showing) (thecircle 12 of) the survey marker 10 appearing in the captured image onthe basis of the feature amount of the candidate region from the featureamount extraction unit 62.

In other words, the discrimination unit 63 discriminates whether thecandidate region includes the survey marker 10 or not on the basis ofthe feature amount of the candidate region.

The discrimination unit 63 then detects the survey marker 10 from thecaptured image obtained from camera 21, on the basis of a discriminationresult of the survey marker 10, and outputs a detection result thereof(e.g., an image of the survey marker 10 or a position of the surveymarker 10 within the captured image).

<Detection Processing>

FIG. 11 is a flowchart for describing an example of the detectionprocessing of detecting the survey marker 10, which is performed by theCPU 32 of the cloud server 30 as the image processing apparatus of FIG.10.

In Step S31, the candidate region extraction unit 61 performs candidateregion extraction processing of extracting the candidate region from thecaptured image obtained from the camera 21.

In the candidate region extraction processing, in Step S31-1, thecandidate region extraction unit 61 binarizes (the pixel value of) eachpixel of the captured image depending on whether the pixel is a pixelhaving a color provided to the circle 12 of the survey marker 10 or apixel having a color other than the color provided to the circle 12.

For example, in a case where the color provided to the circle 12 of thesurvey marker 10 is a red color, the candidate region extraction unit 61determines, by using a hue H (Hue) a hue of the red color, which is thecolor of the circle 12, in the HSV space, a pixel of a hue H consideredto be the hue H of the red color in the range of, for example, 320 to360 (degrees), as the pixel having the color provided to the circle 12,and sets the pixel value thereof to, for example, 1, which is one of 0and 1.

Further, the candidate region extraction unit 61 determines each of thepixels out of the range having the hue H of 320 to 360 (pixels notdetermined as the pixel having the color provided to the circle 12) as apixel that is not determined as the pixel having the color provided tothe circle 12, and sets the pixel value thereof to 0, which is the otherone of 0 and 1.

Note that the pixels of the captured image can be binarized by using thesaturation S (Saturation) and the lightness (luminance) V (Value) inaddition to the hue H, of the HSV space, of the color of the circle 12.

For example, in a case where the color provided to the circle 12 of thesurvey marker 10 is the red color, the pixel having the hue H in therange of 320 to 360 in the HSV space and having the saturation S in therange of 30 to 255 in the HSV space can be determined as a pixel of thecolor provided to the circle 12.

Alternatively, the pixel having the hue H in the range of 320 to 360 inthe HSV space, having the saturation S in the range of 30 to 255 in theHSV space, and having the lightness V in the range of 50 to 255 in theHSV space can be determined as a pixel of the color provided to thecircle 12.

As described above, in the candidate region extraction processing, it ispossible to perform binarization for extracting the candidate region byusing at least the hue among the hue, the saturation, and the lightnessof the color of the circle 12.

Further, in the candidate region extraction processing, when thebinarization for extracting the candidate region is performed by usingat least the hue out of the saturation and the lightness other than thehue of the circle 12, it is possible to extract a more probablecandidate region as a region in which the survey marker 10 appears, andalso improve the detection accuracy of the survey marker 10.

In the candidate region extraction processing, in Step S31-2, thecandidate region extraction unit 61 performs erosion processing on abinarized image obtained by binarizing the captured image and controlsthe noise of the binarized image.

Furthermore, in the candidate region extraction processing, in StepS31-3, the candidate region extraction unit 61 performs dilationprocessing on the binarized image after the erosion processing.

After that, in the candidate region extraction processing, in StepS31-4, the candidate region extraction unit 61 performs outlinedetection processing of detecting the outline of the region of thepixels having the pixel values of 1 in the binarized image obtainedafter the dilation processing, that is, the outline of the region of thepixels, in which it is assumed that the circle 12 appears, in thecaptured image.

In the candidate region extraction processing, in Step S31-5, thecandidate region extraction unit 61 then extracts a region correspondingto a minimum rectangle circumscribed to the outline detected by theoutline detection processing, as a candidate region, from the capturedimage, and supplies the region to the feature amount extraction unit 62.

In a case where there are a plurality of outlines detected by theoutline detection processing, a candidate region is extracted for eachof the plurality of outlines.

In Step S32, the feature amount extraction unit 62 performs featureamount extraction processing of extracting the feature amount of acandidate region for each of candidate regions obtained from thecandidate region extraction unit 61 and supplies the feature amount ofeach candidate region, which is obtained by the feature amountextraction processing, to the discrimination unit 63.

In the feature amount extraction processing, the feature amountextraction unit 62 can extract, for example, the following featureamount of the candidate region.

In other words, the feature amount extraction unit 62 can calculate, asthe feature amount of the candidate region, for example, a ratio of thesize of the candidate region to an estimated size obtained by estimatingthe size of (the circle 12 of) the survey marker 10 when the surveymarker 10 appears in the captured image (hereinafter, the ratio is alsoreferred to as size ratio).

Here, the captured image captured with the camera 21 is recorded in thefile of the EXIF (Exchangeable Image File Format) format, for example.In the file of the EXIF format, an image capturing date and time, afocal length, and GPS information such as the latitude, longitude,altitude (height), or the like of an image capturing position arerecorded as image capturing metadata.

The feature amount extraction unit 62 estimates the size of the surveymarker 10 obtained in a case where the survey marker 10 appears in thecaptured image, for example, from the altitude and the focal length ofthe image capturing position recorded in the file of the EXIF format.

According to the size ratio, a candidate region having a too large ortoo small size can be prevented from being discriminated as (the regionof the circle 12 of) the survey marker 10. For example, as the sizeratio becomes closer to 1.0, the candidate region is more likely to bediscriminated as (the circle 12 of) the survey marker 10.

The feature amount extraction unit 62 can calculate, for example, anaspect ratio of the candidate region as the feature amount of thecandidate region.

According to the aspect ratio of the candidate region, ahorizontally-long or vertically-long candidate region can be preventedfrom being discriminated as the survey marker 10. For example, as theaspect ratio of the candidate region becomes closer to 1.0, thecandidate region is more likely to be discriminated as the survey marker10.

The feature amount extraction unit 62 can calculate, for example, acorrelation (degree of similarity) between the candidate region and atemplate image of (the circles 11 and 12 of) the survey marker 10, asthe feature amount of the candidate region. For example, as thecorrelation between the candidate region and the template image becomeshigher (the candidate region and the template image have a highercorrelation), the candidate region is more likely to be discriminated asthe survey marker 10.

Note that the template image of the survey marker 10 is prepared inadvance.

Further, for example, a correlation coefficient, a mean value ofsquared-sums of the differences, or the like can be employed as thecorrelation.

The feature amount extraction unit 62 can calculate, for example, acorrelation between the candidate region and a rotation image obtainedby rotating the candidate region, as the feature amount of the candidateregion. As the correlation between the candidate region and the rotationimage becomes larger, the candidate region is more likely to bediscriminated as the survey marker 10.

Since the circles 11 to 13 are concentrically disposed, the surveymarker 10 has symmetry. In a case where the survey marker 10 isdiscriminated by using the correlation between the candidate region andthe rotation image obtained by rotating the candidate region, as thefeature amount of the candidate region, the discrimination accuracy ofthe survey marker 10 can be improved by using the symmetry of the surveymarker 10.

Note that when the rotation image is calculated, the rotation of thecandidate image is performed by a predetermined angle other than theinteger multiple of 2n.

For example, the feature amount extraction unit 62 can apply a filter(function), which emphasizes the colors provided to the circles 11 and12, to the candidate region and the template image and calculate thecorrelation between the candidate region and the template image afterthe filter is applied, as the feature amount of the candidate region.For example, as the correlation between the candidate region and thetemplate image after the filter is applied becomes larger, the candidateregion is more likely to be discriminated as the survey marker 10.

Note that in addition to the filter that emphasizes the colors providedto the circles 11 and 12, for example, a filter that emphasizes thecolors provided to only one of the circles 11 and 12, or the like can beemployed as the filter applied to the candidate region and the templateimage.

The feature amount extraction unit 62 can calculate a distance betweenthe hues of the respective circles 11 and 12 as feature amount of thecandidate region.

In other words, assuming that the candidate region is a regioncircumscribed to the circle 12, the feature amount extraction unit 62can calculate, as feature amount of the candidate region, the distancebetween the hues of the respective circles 11 and 12 described in FIG.7, for example, the distance DF of Expression (1), by using the hues ofthe pixels in which the circles 11 and 12 considered to exist in thecandidate region appear.

For example, in a case where the distance DF between the hues of therespective circles 11 and 12 is equal to or larger than the threshold THof Expression (3), the candidate region is more likely to bediscriminated as the survey marker 10.

In Step S33, the discrimination unit 63 discriminates, for eachcandidate region, (the region showing) (the circle 12 of) the surveymarker 10 appearing in the captured image, in the captured image on thebasis of the feature amount of the candidate region from the featureamount extraction unit 62.

In other words, the discrimination unit 63 discriminates whether thecandidate region includes the survey marker 10 or not on the basis ofthe feature amount of the candidate region.

Furthermore, in a case of discriminating that the candidate regionincludes the survey marker 10, the discrimination unit 63 detects thesurvey marker 10 from the captured image obtained from the camera 21 onthe basis of a discrimination result and outputs a detection result.

In the cloud server 30, as described in FIG. 2, a three-dimensionalmodel of the ground is created by using the detection result of thesurvey marker 10 thus obtained.

Here, in the discrimination unit 63, an arbitrary method can be employedas the method of discriminating whether the candidate region includesthe survey marker 10 or not on the basis of the feature amount of thecandidate region. For example, whether the candidate region includes thesurvey marker 10 or not can be discriminated by performing thresholdprocessing for each feature amount of the candidate region, a decisionby majority of a processing result of the threshold processing,weighting addition of a point representing the processing result, or thelike. Further, for example, each feature amount of the candidate regioncan be input to a discriminator including a neural network in whichlearning is performed in advance, and whether the candidate regionincludes the survey marker 10 or not can be discriminated on the basisof an output of the discriminator in response to the input.

Note that the feature amount of the candidate region extracted in thefeature amount extraction unit 62 is not limited to the feature amountsdescribed above.

However, when the feature amount of the candidate region includes thedistance DF between the hues of the respective circles 11 and 12, thesurvey marker 10 can be detected more accurately.

In other words, for example, in a case where the color of the circle 12is a red color, the candidate region extraction unit 61 performs(binarization for) the detection of the candidate region by using atleast the hue of the circle 12. Thus, for example, the region in which apylon of the red color appears may be extracted as a candidate region.In this case, when the feature amount of the candidate region does notinclude the distance DF between the hues of the respective circles 11and 12, the possibility of erroneously discriminating that a candidateregion in which the pylon appears includes the survey marker 10 isincreased.

Meanwhile, when the feature amount of the candidate region includes thedistance DF between the hues of the respective circles 11 and 12, thepossibility of erroneously discriminating that a candidate region inwhich the pylon appears includes the survey marker 10 can be suppressed,and the detection accuracy of the survey marker 10 can be improved.

Note that, as described in FIG. 6, in a case where the columnar member11 of the survey marker 10 integrates the illuminance detectionapparatus that detects the illuminance of the survey marker 10, thecommunication apparatus that performs wireless communication, and thelike, the cloud server 30 can perform the detection processing of FIG.11 by using the illuminance information regarding the illuminance of thesurvey marker 10 (distribution of the illuminance (luminance) of thesurvey marker 10) detected by the illuminance detection apparatus fromthe survey marker 10.

For example, the candidate region extraction unit 61 can extract thecandidate region by using the illuminance information.

For example, the candidate region extraction unit 61 can estimate arange of the hue, the saturation, and the lightness of the color of thecircle 12 of the survey marker 10 appearing in the captured image byusing the illuminance information, determine a pixel having the hue, thesaturation, and the lightness within the range, as the pixel of thecircle 12, and perform (the binarization for) the extraction of thecandidate region.

Further, for example, the discrimination unit 63 can discriminate thesurvey marker 10 by using the illuminance information.

Specifically, for example, the discrimination unit 63 compares thedistance DF between the hues of the respective circles 11 and 12, whichis the feature amount of the candidate region, with the threshold TH ofExpression (3). In a case where the distance DF has the threshold TH ormore on the basis of a comparison result, the survey marker 10 can bediscriminated by increasing the possibility of discriminating that thecandidate region includes the survey marker 10.

In the discrimination unit 63, the threshold TH descried above, which isused to discriminate the survey marker 10, can be set using theilluminance information.

In other words, the discrimination unit 63 can estimate the hues of therespective pixels of the circles 11 and 12, which are obtained in a casewhere an image of the survey marker 10 is captured under a illuminancecondition indicated by the illuminance information, use mean values of(the estimated values of) the hues, of the respective pixels of thecircles 11 and 12, obtained by the estimation, as H₁ and H₂ ofExpression (3), and set the threshold TH of Expression (3).

As described above, by using the illuminance information of the surveymarker 10 detected by the illuminance detection apparatus to extract thecandidate region or discriminate the survey marker 10, the detectionaccuracy of the survey marker 10 can be improved.

FIG. 12 is a flowchart for describing an example of detailed processingof binarizing each pixel of the captured image, which is performed inStep S31-1 of FIG. 11.

In Step S51, the candidate region extraction unit 61 selects one of thepixels of the captured image, which is not yet selected as a pixel ofinterest, as a pixel of interest, and the processing proceeds to StepS52.

In Step S52, the candidate region extraction unit 61 calculates and thenacquires the hue H of the pixel of interest, and the processing proceedsto Step S53.

In Step S53, the candidate region extraction unit 61 determines whetherthe hue H of the pixel of interest is considered as the hue of the colorof the circle 12 or not, that is, whether the hue H of the pixel ofinterest satisfies an expression of α<H and an expression of H<β or not.

Here, α and β represent the minimum value and the maximum value,respectively, in the range considered as the hue of the color of thecircle 12.

In Step S53, in a case where it is determined that the hue H of thepixel of interest satisfies the expression of α<H and the expression ofH<β, the processing proceeds to Step S54. In Step S54, the candidateregion extraction unit 61 assumes that the pixel of interest is thepixel of the hue of the circle 12 and sets the pixel value of the pixelof interest to 1 representing the pixel of the hue of the circle 12. Theprocessing proceeds to Step S56.

Further, in Step S53, in a case where it is determined that the hue H ofthe pixel of interest does not satisfy at least one of the expression ofα<H or the expression of H<β, the processing proceeds to Step S55. InStep S55, the candidate region extraction unit 61 assumes that the pixelof interest is not the pixel of the hue of the circle 12 and sets thepixel value of the pixel of interest to 0 not representing the pixel ofthe hue of the circle 12. The processing proceeds to Step S56.

In Step S56, the candidate region extraction unit 61 determines whetherall the pixels of the captured image are selected as the pixel ofinterest or not.

In Step S56, when it is determined that all the pixels of the capturedimage are not yet selected as the pixel of interest, the processingreturns to Step S51. In Step S51, the candidate region extraction unit61 newly selects one of the pixels of the captured image, which is notyet selected as the pixel of interest, as the pixel of interest, andsimilar processing is repeated thereafter.

Further, in Step S56, when it is determined that all the pixels of thecaptured image are each selected as the pixel of interest, thebinarizing processing is terminated.

FIG. 13 is a diagram for showing an example of a template image of (thecircles 11 and 12 of) the survey marker 10, which is used for extractingthe feature amount of the candidate region in the feature amountextraction unit 62.

Now, a Gaussian function defined by coefficients a, μ, and σ isexpressed as Gaussian(a, μ, σ) as shown in Expression (4).

$\begin{matrix}\left\lbrack {{Math}.\mspace{14mu} 4} \right\rbrack & \; \\{{{Gaussian}\mspace{14mu} \left( {a,\mu,\sigma} \right)} = {a\mspace{14mu} \exp \left\{ {- \frac{\left( {x - \mu} \right)^{2}}{2\; \sigma^{2}}} \right\}}} & (4)\end{matrix}$

In a case where, for example, a blue color and a red color are employedfor the colors of the circles 11 and 12, respectively, an image definedby the Gaussian function shown in, for example, FIG. 13 can be employedas the template image.

A of FIG. 13 shows a first example of the template image, and B of FIG.13 shows a second example of the template image.

Now, the hue as the pixel value of the template image is represented byy, and a variable x of the Gaussian function Gaussian(a, μ, σ) ofExpression (4) represents a distance from the center of the templateimage.

In this case, the hue y of the template image in A of FIG. 13 isrepresented by the expression of y=360-Gaussian(a=50, μ=0, σ=0.3).Further, the hue y of the template image in B of FIG. 13 is representedby the expression of y=360-Gaussian(a=100, μ=0, σ=0.3).

FIG. 14 is a diagram showing an example of filters that are used whenthe colors provided to the circles 11 and 12 in each of the candidateregion and the template image are emphasized in the feature amountextraction processing in Step S32 of FIG. 11.

For example, the colors provided to the circles 11 and 12 are the bluecolor and the red color, respectively, a filter that emphasizes thecolor provided to the circle 11 is a blue filter that emphasizes theblue color, and a filter that emphasizes the color provided to thecircle 12 is a red filter that emphasizes the red color.

Now, the hue as the output of the filter is represented by y, and avariable x of the Gaussian function Gaussian(a, μ, σ) of Expression (4)represents a hue to be input to the filter.

In this case, the red filter is represented by the expression ofy=Gaussian(a=255, μ=10, σ=20) (in a case where x is a value in the rangeof 10<=x<=180), the expression of y=Gaussian(a=255, μ=350, σ=20) (in acase where x is a value in the range of 180<=x<=350), and the expressionof y=255 (in a case where x is a value in other ranges).

Further, the blue filter is represented by the expression ofy=Gaussian(a=128, μ=270, σ=40).

Note that in FIG. 14, the solid line represents input/outputcharacteristics of the red filter, and the dotted line representsinput/output characteristics of the blue filter.

Further, in FIG. 14, an image P1 is an image obtained by resizing acandidate region, in which the circle 12 appears and the hue H isemployed as a pixel value, to 50×50 pixels and extracting 30×30 pixelslocated at the center. Furthermore, an image Q1 is an image obtained byresizing a candidate region, in which the circle 12 does not appear andthe hue H is employed as a pixel value, to 50×50 pixels and extracting30×30 pixels located at the center.

Images P2 and Q2 are images obtained when the blue filter is applied tothe images P1 and Q1, respectively, and images P3 and Q3 are imagesobtained when the red filter is applied to the images P1 and Q1,respectively.

FIG. 15 is a flowchart for describing an example of the processing ofextracting the distance DF between the hues of the respective circles 11and 12 as a feature amount in the feature amount extraction processingperformed in Step S32 of FIG. 11.

In Step S71, the feature amount extraction unit 62 assumes that thecandidate region is a region circumscribed to the circle 12, and detectsthe pixels showing (supposed to show) the respective circles 11 and 12existing in the candidate region (hereinafter, referred to as a pixel inthe region of the circle 11 and as a pixel in the region of the circle12). The processing proceeds to Step S72.

In Step S72, the feature amount extraction unit 62 calculates andacquires the hue H of each pixel in the region of the circle 11 and alsocalculates and acquires the hue H of each pixel in the region of thecircle 12. The processing proceeds to Step S73.

In Step S73, according to Expression (1), the feature amount extractionunit 62 calculates, as the distance between the hues of the respectivecircles 11 and 12, an absolute difference value between a mean value ofthe hues H of the respective pixels in the region of the circle 11(ΣH_(i, j)/N1 in the first term on the right side of Expression (1)) anda mean value of the hues H of the respective pixels in the region of thecircle 12 (ΣH_(i, j)/N2 in the second term on the right side ofExpression (1)). The processing is then terminated.

Note that, for example, the single-circle-type marker in FIG. 4 and themulti-circle-type marker in FIG. 5 or 6 can be installed as the surveymarker 10 in combination, and in a case where the single-circle-typemarker can be detected with sufficient accuracy, the detection of thesingle-circle-type marker can be performed without performing thedetection of the multi-circle-type marker, and in a case where thesingle-circle-type marker cannot be detected with sufficient accuracy,the detection of the multi-circle-type marker can be performed.

<Configuration Example of Drone 20>

FIG. 16 is a block diagram showing a configuration example of the drone20 shown in FIG. 1.

In FIG. 16, the drone 20 includes a communication unit 111, a controlunit 112, a drive control unit 113, and a flight mechanism 114.

The communication unit 111 performs communication wirelessly or by wirewith the cloud server 30, a controller (proportional control system)(not shown) that operates the drone 20, or another arbitrary apparatusunder the control of the control unit 112.

The control unit 112 includes a CPU, a memory, or the like (not shown)and controls the communication unit 111, the drive control unit 113, andthe camera 21.

Further, the control unit 112 causes the communication unit 111 totransmit the captured image that is captured with the camera 21.

The drive control unit 113 controls the drive of the flight mechanism114 under the control of the control unit 112.

The flight mechanism 114 is a mechanism for causing the drone 20 to flyand includes, for example, a motor, a propeller, or the like (notshown). The flight mechanism 114 is driven under the control of thedrive control unit 113 and causes the drone 20 to fly.

In the drone 20 thus configured, the control unit 112 controls the drivecontrol unit 113 to drive the flight mechanism 114 according to a signalfrom the proportional control system, which is received in thecommunication unit 111, for example. Accordingly, the drone 20 fliesaccording to the operation of the proportional control system.

Further, the control unit 112 controls the camera 21 to perform imagecapturing according to a signal from the proportional control system. Acaptured image obtained by image capturing by the camera 21 istransmitted from the communication unit 111 via the control unit 112.

<Another Embodiment of Soil-Volume Measurement System to Which PresentTechnology is Applied>

FIG. 17 is a diagram for describing the general outline of anotherembodiment of a soil-volume measurement system to which the presenttechnology is applied.

Note that portions in the figure that correspond to those of FIG. 1 aredenoted by the same reference symbols, and description thereof will behereinafter omitted appropriately.

The soil-volume measurement system of FIG. 17 includes survey markers10, a drone 20, a cloud server 30, and a flight control apparatus 121.

Therefore, the soil-volume measurement system of FIG. 17 is differentfrom the case of FIG. 1 in that the flight control apparatus 121 isnewly provided.

The flight control apparatus 121 includes a dedicated apparatus thatfunctions as GCS (Ground Control Station) (Ground Station).Alternatively, the flight control apparatus 121 is configured when a PC(Personal Computer), a tablet, or an apparatus having a communicationfunction, such as a smartphone, executes a program for causing such anapparatus to function as the GCS.

According to an operation by an operator, the flight control apparatus121 performs communication with the drone 20 and performs the control ofthe flight of the drone 20, the acquisition of a position, an imagecapturing command for the camera 21 equipped with the drone 20, acommand of acquisition of a captured image captured with the camera 21,or the like.

According to an operation by the operator, the flight control apparatus121 can perform detection processing of detecting (an image of) a surveymarker 10 from the captured image acquired from the drone 20, anddisplay a detection result of the survey marker 10, which is obtained inthe detection processing. The operator can confirm whether an image ofthe survey marker 10 is suitably captured or not from the detectionresult of the survey marker 10.

In a case where an image of the survey marker 10 is not suitablycaptured, for example, in a case where the survey marker 10 cannot bedetected in the detection processing, the operator can operate theflight control apparatus 121 to cause the drone 20 to fly again andcapture an image of the survey marker 10.

Note that the flight control apparatus 121 can upload the captured imageacquired from the drone 20 to the cloud server 30.

Further, for example, as described in FIG. 6 or the like, in a casewhere the survey marker 10 integrates the illuminance detectionapparatus or the like and transmits illuminance information detected bythe illuminance detection apparatus, the illuminance information can bereceived by the flight control apparatus 121.

<Modified Examples of Survey Marker 10 of Multi-circle-type Marker>

FIG. 18 is a plan view showing a first modified example of the surveymarker 10 of the multi-circle-type marker.

The survey marker 10 of FIG. 18 includes circles 11 and 12 (or circles12 and 13) and a frame region 14 and has a configuration in which thecircle 13 (or circle 11) is not provided to the survey marker 10 in A ofFIG. 5.

Note that the survey marker 10 of FIG. 18 has a configuration in whichthe frame region 14 is provided to the survey marker 10 in C (or D) ofFIG. 5.

In FIG. 18, for example, a black color of an achromatic color, a redcolor of a chromatic color, and the black color of the achromatic colorcan be employed as the colors of the circles 11 and 12 (or circle 12 and13) and the frame region 14, respectively.

FIG. 19 is a perspective view showing a second modified example of thesurvey marker 10 of the multi-circle-type marker.

The survey marker 10 of FIG. 19 includes, for example, a columnar member201 that is to be the circle 11 and has a predetermined height(thickness), a substantially annular member 202 that is to be the circle12 and has a predetermined height, and a substantially annular member203 that is to be the circle 13 and has a predetermined height.

In FIG. 19, the members 201 to 203 have the same height.

Additionally, the member 202 has a substantially annular shape obtainedby hollowing out the center portion of the column having thepredetermined height into a columnar shape, and the columnar member 201is fitted into the hollow portion, of the member 202, which is hollowedout into the columnar shape.

Similarly, the member 203 has a substantially annular shape obtained byhollowing out the center portion of the column having the predeterminedheight into a columnar shape, and the substantially annular member 202is fitted into the hollow portion, of the member 203, which is hollowedout into the columnar shape.

In the survey marker 10 of FIG. 19, the inside of the member 201, 202,or 203 can be configured to be hollow, and the illuminance detectionapparatus or the like described in FIG. 6 or the like can be integratedin the member 201, 202, or 203.

Further, the illuminance detection apparatus or the like can beintegrated across the multiple members 201 to 203.

In the survey marker 10 of FIG. 6, the height (thickness) of thecolumnar member that is to be the circle 11 protrudes as compared to thecircular member that is to be the circle 12 and the circular member thatis to be the circle 13. Therefore, depending on the direction ofsunlight, the shadow of the columnar member that is to be the circle 11is largely formed on the circle 12, and the detection accuracy of thesurvey marker 10 may degrade.

Meanwhile, in the survey marker 10 of FIG. 19, the members 201 to 203have the same height, and thus the shadow of the columnar member 201that is to be the circle 11 is not largely formed on the circle 12 as inthe case of FIG. 6. This can suppress degradation of the detectionaccuracy of the survey marker 10.

Note that the survey marker 10 of FIG. 19 includes the columnar member201 and the substantially annular members 202 and 203, or alternatively,for example, can be configured by painting the upper surface of onecolumnar member having a predetermined height in the colors of thecircles 11 to 13.

In addition thereto, the survey marker 10 of FIG. 19 can be configuredby, for example, painting one columnar member having a predeterminedheight in the colors of the circles 11 and 12 and fitting the memberinto the substantially annular member 203, or painting a substantiallyannular member in the colors of the circles 12 and 13 and fitting themember 201 into it.

Here, for example, a black color of an achromatic color, a red color ofa chromatic color, and the black color of the achromatic color arehereinafter employed as the colors of the circles 11 to 13,respectively.

FIG. 20 is a perspective view showing a third modified example of thesurvey marker 10 of the multi-circle-type marker.

Note that portions in the figure that correspond to those of FIG. 19 aredenoted by the same reference symbols, and description thereof will behereinafter omitted appropriately.

The survey marker 10 of FIG. 20 includes, for example, a columnar member201 that is to be the circle 11 and has a predetermined height, asubstantially annular member 202 that is to be the circle 12 and has apredetermined height, and a substantially annular member 213 that is tobe the circle 13 and has a predetermined height.

Therefore, the survey marker 10 of FIG. 20 is different from the case ofFIG. 19 in that the member 213 is provided instead of the member 203.

The member 213 has a substantially annular shape obtained by hollowingout the center portion of the column having a predetermined height intoa columnar shape with a bottom plate 213A being left, or a substantiallyannular shape obtained by hollowing out the center portion of the columnhaving a predetermined height into a columnar shape and providing thebottom plate 213A.

Additionally, the substantially annular member 202 is fitted into thehollow portion, of the member 203, which is hollowed out into thecolumnar shape, and the columnar member 201 is configured to beattachable/detachable to/from the hollow portion, of the member 202,which is hollowed out into a columnar shape.

Note that the bottom plate 213A has the same color as that of the member201, i.e., here, the black color of the achromatic color, such that thebottom plate 213A exposed from the hollow portion of the member 202functions as the circle 11 in the survey marker 10 when the columnarmember 201 is removed.

Further, the depth of the hollow portion, of the member 213, which ishollowed out into the columnar shape, has the same depth as the heightof the members 201 and 202. Therefore, when the member 202 (and member201) is fitted into the hollow portion of the member 213, the uppersurface of the survey marker 10 becomes planar.

In the survey marker 10 of FIG. 20, the inside of the member 201 can beconfigured to be hollow, and the illuminance detection apparatus or thelike described in FIG. 6 or the like can be integrated in the member201.

In a case where the illuminance information is necessary, the surveymarker 10 can be used after the columnar member 201 is mounted in thehollow portion, of the member 202, which is hollowed out into thecolumnar shape.

Meanwhile, in a case where the illuminance information is not necessary,the survey marker 10 can be used after the columnar member 201 isremoved from the survey marker 10.

In the survey marker 10 in which the columnar member 201 is removed, theshadow of the member 202 may be formed on the exposed bottom plate 213A.However, the color of the bottom plate 213A is the black color here, andthus the shadow of the member 202 that may be formed on the exposedbottom plate 213A does not (substantially) affect the detection accuracyof the survey marker 10.

Note that the portion including the members 202 and 213 in FIG. 20 canbe configured by, for example, painting a member in the colors of thecircles 11 to 13, the member being obtained by hollowing out the centerportion of a single columnar member having a predetermined height into acolumnar shape while leaving the bottom plate 213A so as to be capableof attaching/detaching the member 201.

FIG. 21 is a perspective view showing a fourth modified example of thesurvey marker 10 of the multi-circle-type marker.

Note that portions in the figure that correspond to those of FIG. 20 aredenoted by the same reference symbols, and description thereof will behereinafter omitted appropriately.

The survey marker 10 of FIG. 21 includes a member 202 and a member 213.

Therefore, the survey marker 10 of FIG. 21 configured to be similar tothe case of FIG. 20 except that the attachable/detachable member 201 isnot provided.

In the survey marker 10 of FIG. 21, the inside of the member 202 or 213can be configured to be hollow, and the illuminance detection apparatusor the like described in FIG. 6 or the like can be integrated in themember 202 or 213.

Further, the illuminance detection apparatus or the like can beintegrated across the members 202 and 213.

In the survey marker 10 of FIG. 21, the shadow of the member 202 may beformed on the exposed bottom plate 213A as in the case of the surveymarker 10 of FIG. 20 when the columnar member 201 is removed. However,the color of the bottom plate 213A is the black color here, and thus theshadow of the member 202 that may be formed on the exposed bottom plate213A does not affect the detection accuracy of the survey marker 10.

FIG. 22 is a perspective view showing a fifth modified example of thesurvey marker 10 of the multi-circle-type marker.

Note that portions in the figure that correspond to those of FIG. 19 aredenoted by the same reference symbols, and description thereof will behereinafter omitted appropriately.

The survey marker 10 of FIG. 22 includes members 201 and 202 and aplate-like circular member 223 that is to be the circle 13.

Therefore, the survey marker 10 of FIG. 22 is configured to be similarto the case of FIG. 19 except that the member 223 is provided instead ofthe member 203.

The survey marker 10 of FIG. 22 is configured by, for example, fittingthe member 201 into the member 202 and stacking the member 202, intowhich the member 201 is fitted (or single columnar member configured asthe circles 11 and 12), on the member 223.

In the survey marker 10 of FIG. 22, the inside of the member 201 or 202can be configured to be hollow, and the illuminance detection apparatusor the like described in FIG. 6 or the like can be integrated in themember 201 or 202.

Further, the illuminance detection apparatus or the like can beintegrated across the members 201 and 202.

In the survey marker 10 of FIG. 22, the shadow of the member 202 may beformed on the member 223. However, the color of the member 223 that isto be the circle 13 is the black color here, and thus the shadow of themember 202 that may be formed on the member 223 does not affect thedetection accuracy of the survey marker 10.

FIG. 23 is a perspective view showing a sixth modified example of thesurvey marker 10 of the multi-circle-type marker.

Note that portions in the figure that correspond to those of FIG. 22 aredenoted by the same reference symbols, and description thereof will behereinafter omitted appropriately.

The survey marker 10 of FIG. 23 includes members 201 and 202 and amember 223.

However, the member 201 is configured to be attachable/detachableto/from the hollow portion, of the member 202, which is hollowed outinto a columnar shape.

Further, when the member 201 is removed from the hollow portion of themember 202, a circular portion 223A as a part of the member 223 isexposed from the hollow portion. The circular portion 223A has the samecolor as that of the member 201, i.e., here, the black color of theachromatic color such that the circular portion 223A functions as thecircle 11.

In the survey marker 10 of FIG. 23, the inside of the member 201 can beconfigured to be hollow, and the illuminance detection apparatus or thelike described in FIG. 6 or the like can be integrated in the member201.

In a case where the illuminance information is necessary, the surveymarker 10 can be used after the columnar member 201 is mounted in thehollow portion, of the member 202, which is hollowed out into thecolumnar shape.

Meanwhile, in a case where the illuminance information is not necessary,the survey marker 10 can be used after the columnar member 201 isremoved from the survey marker 10.

In the survey marker 10 in which the columnar member 201 is removed, theshadow of the member 202 may be formed on the exposed circular portion223A. Further, irrespective of the attachment/detachment of the member201, the shadow of the member 202 may be formed on the member 223.

However, the color of the member 223 including the circular portion 223Ais the black color here, and thus the shadow of the member 202 that maybe formed on the member 223 including the circular portion 223A does notaffect the detection accuracy of the survey marker 10.

FIG. 24 is a perspective view showing a seventh modified example of thesurvey marker 10 of the multi-circle-type marker.

Note that portions in the figure that correspond to those of FIG. 23 aredenoted by the same reference symbols, and description thereof will behereinafter omitted appropriately.

The survey marker 10 of FIG. 24 is configured to be similar to the caseof FIG. 23 except that the attachable/detachable member 201 is notprovided.

In the survey marker 10 of FIG. 24, the inside of the member 202 can beconfigured to be hollow, and the illuminance detection apparatus or thelike described in FIG. 6 or the like can be integrated in the member202.

In the survey marker 10 of FIG. 24, as in the case of FIG. 23, theshadow of the member 202 may be formed on the circular portion 223A andthe member 223. However, the color of the member 223 including thecircular portion 223A is the black color, and thus the shadow of themember 202 does not affect the detection accuracy of the survey marker10.

FIG. 25 is a perspective view showing an eighth modified example of thesurvey marker 10 of the multi-circle-type marker.

Note that portions in the figure that correspond to those of FIG. 20 aredenoted by the same reference symbols, and description thereof will behereinafter omitted appropriately.

The survey marker 10 of FIG. 25 includes a member 213.

As described in FIG. 20, the member 213 has a substantially annularshape obtained by hollowing out the center portion of the column havinga predetermined height into a columnar shape with a bottom plate 213Abeing left, or a substantially annular shape obtained by hollowing outthe center portion of the column having a predetermined height into acolumnar shape and providing the bottom plate 213A.

However, in FIG. 25, the bottom plate 213A of the member 213 is paintedso as to function as the circles 11 and 12. In other words, here, acircular region at the center portion of the bottom plate 213A has theblack color so as to function as the circle 11, and a circularcircumferential region thereof has the red color so as to function asthe circle 12.

In the survey marker 10 of FIG. 25, the inside (portion functioning asthe circle 13) of the member 213 can be configured to be hollow, and theilluminance detection apparatus or the like described in FIG. 6 or thelike can be integrated in the member 213.

FIG. 26 is a perspective view showing a ninth modified example of thesurvey marker 10 of the multi-circle-type marker.

Note that portions in the figure that correspond to those of FIG. 25 aredenoted by the same reference symbols, and description thereof will behereinafter omitted appropriately.

The survey marker 10 of FIG. 26 includes a columnar member 231 that isto be the circles 11 and 12 and has a predetermined height, and a member213.

The member 231 has a shape (columnar shape) similar to, for example, themember 202 of FIG. 19, in which the member 201 is fitted. Further, themember 231 is painted to be the circles 11 and 12 so as to function asthe circles 11 and 12.

In the survey marker 10 of FIG. 26, the member 231 isattachable/detachable to/from the hollow portion of the member 213.

In the survey marker 10 of FIG. 26, the inside of the member 231 can beconfigured to be hollow, and the illuminance detection apparatus or thelike described in FIG. 6 or the like can be integrated in the member231.

In a case where the illuminance information is necessary, the surveymarker 10 can be used after the member 231 is mounted in the hollowportion of the member 213.

Meanwhile, in a case where the illuminance information is not necessary,the survey marker 10 can be used after the member 231 is removed fromthe survey marker 10.

Note that the member 231 includes the single columnar member, oralternatively, for example, can include the members 201 and 202 of FIG.19.

FIG. 27 is a perspective view showing a tenth modified example of thesurvey marker 10 of the multi-circle-type marker.

Note that portions in the figure that correspond to those of FIG. 19 or25 are denoted by the same reference symbols, and description thereofwill be hereinafter omitted appropriately.

The survey marker 10 of FIG. 27 is configured by, for example, stackingthe member 201 in a region of the bottom plate 213A of the member 213,the region being to be the circle 11.

In the survey marker 10 of FIG. 27, the inside (portion functioning asthe circle 13) of the member 201 or the member 213 can be configured tobe hollow, and the illuminance detection apparatus or the like describedin FIG. 6 or the like can be integrated in the member 201 or the member213.

Further, in the survey marker 10 of FIG. 27, the members 201 and 213 canbe configured to be hollow, and the illuminance detection apparatus andthe like can be integrated separately in the member 201 and the member213.

Note that as shown in FIG. 27, the survey marker 10 in which the circles11 and 13 each have a certain thickness is not limited to theconfigurations described above. In other words, the survey marker 10 inwhich the circles 11 and 13 each have a certain thickness can beconfigured, for example, such that one or both of the portion that is tobe the circle 11 and the portion that is to be the circle 13 areattachable/detachable.

FIG. 28 is a perspective view showing an eleventh modified example ofthe survey marker 10 of the multi-circle-type marker.

The survey marker 10 of FIG. 28 includes a plate-like member 250 havinga certain thickness.

The circle 11 to the circle 13 are depicted by printing or the like onthe upper surface of the member 250.

The member 250 is made of, for example, a translucent material in awhite color or the like, and can be configured to be hollow.

For example, a lighting apparatus (not shown) can be integrated in themember 250.

In this case, when the lighting apparatus is turned on, the surveymarker 10 can be caused to light.

When the survey marker 10 is caused to light, image capturing in a statewhere the survey marker 10 is detectable can be performed even in a darksituation such as night, and the survey marker 10 can be used as alandmark or the like for landing of the drone 20.

FIG. 29 is a plan view showing a twelfth modified example of the surveymarker 10 of the multi-circle-type marker.

In the survey marker 10 of FIG. 29, for example, a portion of the circle12 in the red color and a portion of the frame region 14 in the whitecolor include, for example, a luminous body such as an LED (LightEmitting Diode).

When the portion of the circle 12 in the red color and the portion ofthe frame region 14 in the white color are caused to light, imagecapturing in a state where the survey marker 10 is detectable can beperformed even in a dark situation such as night, and the survey marker10 can be used as a landmark or the like for landing of the drone 20.

FIG. 30 is a perspective view showing a thirteenth modified example ofthe survey marker 10 of the multi-circle-type marker.

In the survey marker 10 of FIG. 30, a pattern of the survey marker 10 isdepicted by printing or the like on a landing pad for a drone.Therefore, the survey marker 10 of FIG. 30 functions as a survey markerand also as a landing pad.

Here, the landing pad can be used for preventing sand or the like on theground from being raised at the takeoff and landing of the drone andfrom entering the motor or the like of the drone, and for clarifying thelanding place of the drone, for example.

According to the survey marker 10 of FIG. 30, in the drone 20, theposition of the landing pad that is the survey marker 10 can be detectedfrom the captured image.

Furthermore, according to the survey marker 10 of FIG. 30, in the drone20, after a position at which the camera 21 is to be attached is takeninto consideration, a flight state is controlled such that the surveymarker 10 appearing in a captured image captured with the camera 21 isalways at a constant position, the takeoff and landing perpendicular tothe survey marker 10 can be performed, and the convenience of thelanding pad that is the survey marker 10 can be enhanced.

Further, according to the survey marker 10 of FIG. 30, a pattern changeof the survey marker 10 that appears in the captured image, or the pointof time of the takeoff and landing of the drone after the takeoff andlanding is recognized can be recorded. Such a point of time can be usedfor automatically creating the report of the soil-volume measurement orthe like.

Other Embodiments

In the above description, the marker having a planar shape and includinga plurality of circles concentrically disposed and each having adifferent radius, the circles adjacent to each other among the pluralityof circles each having a different hue, is employed as the survey marker10. However, a marker having a planar shape and including a plurality ofcircles concentrically disposed and each having a different radius, thecircles adjacent to each other among the plurality of circles eachhaving a different luminance or hue, can be employed as the surveymarker 10.

In other words, in the embodiment in FIGS. 1 to 30, the “hue” can bereplaced with the “luminance or hue”.

For example, in FIG. 5 or the like, circles that are adjacent to eachother and each have a different luminance or hue can be employed as thecircles 11 to 13.

Note that, in the survey marker 10, the adjacent circles only need toeach have a different luminance or hue. Therefore, if the adjacentcircles 11 and 12 each have a different luminance or hue and theadjacent circles 12 and 13 each have a different luminance or hue, thecircles 11 and 13 that are not adjacent to each other may have the sameluminance or hue.

Further, in the survey marker 10, the adjacent circles may be differentin luminance only or in hue only, or may be different in both ofluminance and hue.

In a case where the survey marker 10 is detected by using a distancebetween the luminances of the respective circles 11 and 12 adjacent toeach other or by using a distance between the hues as necessary, it iseffective that the combination of the colors of the respective circles11 and 12 is a combination whose possibility of existing in nature is aslow as possible.

Furthermore, it is effective that the combination of the colors of therespective circles 11 and 12 is a combination in which the luminances orhues of the respective colors are as different as possible.

Further, it is effective that the combination of the colors of therespective circles 11 and 12 is a combination in which the degree of thecolor mixture is as low as possible when image capturing is performedfrom a certain height, i.e., for example, a combination in which adistance between the luminance or hue of the circle 11 and the luminanceor hue of the circle 12, which are obtained from the captured image, isas large as possible.

Here, for the distance between the hues of the respective circles 11 and12, as described in FIG. 7, a distance between the peaks of tworespective distributions (difference in hue between peaks) existing inthe hue histogram can be employed.

Similarly, for the distance between the luminances of the respectivecircles 11 and 12, a distance between the peaks of two respectivedistributions (difference in luminance between peaks) in a luminancehistogram for the pixels of the circles 11 and 12 detected from thecaptured image can be employed, the two respective distributionsincluding a distribution with a peak of a first luminance and adistribution with a peak of a second luminance.

Further, for the distance between the hues of the respective circles 11and 12, for example, as described in FIG. 7, a difference in integratedvalue such as a mean value of the hues of the respective pixels betweenthe circles 11 and 12 detected from the captured image (e.g., DF or thelike in Expression (1)) can be employed. However, similarly, adifference in integrated value such as a mean value of the luminances ofthe respective pixels between the circles 11 and 12 detected from thecaptured image can also be employed as the distance between theluminances of the respective circles 11 and 12.

Hereinafter, description will be given on the detection of the surveymarker 10, which is performed by using the luminance, in addition, thehue as necessary.

FIG. 31 is a diagram showing an HLS color space.

In an HLS color space 300, the vertical axis represents a luminance L,and a distance from the axis of the luminance L (hereinafter, alsoreferred to as luminance axis) on a two-dimensional plane perpendicularto the luminance axis represents a saturation S. Further, the angleabout the luminance axis represents a hue H. A point on the luminanceaxis represents an achromatic color.

FIG. 32 is a diagram for describing the general outline of the detectionof the survey marker 10 by using the luminance.

The image processing apparatus of FIG. 10 can distinguish between theregion of the survey marker 10 and other regions from the candidateregion by using a distance between the luminances (luminance difference)of (regions assumed (estimated) as regions of) the respective circles 11and 12 of the survey marker 10 appearing in the captured image.

For example, in a case where the circle 11 is in the black color and thecircle 12 is in the red color, it is possible to relatively accuratelydistinguish between the region of the survey marker 10 and other regionsfrom the candidate region by using the distance between the luminancesof the respective circles 11 and 12.

Further, in a case where the distance between the luminances of therespective circles 11 and 12 is small, the image processing apparatus ofFIG. 10 can distinguish between the region of the survey marker 10 andother regions from the candidate region by using a distance between thehues (hue difference) of the respective circles 11 and 12 of the surveymarker 10 appearing in the captured image.

For example, in a case where the circle 11 has a chromatic color such asa blue color and the circle 12 has another chromatic color such as a redcolor, if the distance between the luminances of the respective circles11 and 12 is small, the image processing apparatus can distinguishbetween the region of the survey marker 10 and other regions from thecandidate region by using the distance between the hues of therespective circles 11 and 12.

The detection of the survey marker 10 by using the luminance can beperformed by using the distance between the luminances of the respectivecircles 11 and 12 adjacent to each other, or alternatively, by using thedistance between the luminances of the respective circles 12 and 13adjacent to each other or the distance between the luminances of therespective circles 11 and 13 that are not adjacent to each other.

FIG. 33 is a diagram for describing the general outline of the detectionof the survey marker 10 by using the luminance in a case where thesurvey marker 10 includes the circles 11 to 13.

Here, circles having the black color, the red color, and the black colorcan be employed as the circles 11 to 13, respectively.

In a case where the survey marker 10 includes the circles 11 to 13, thedetection of the survey marker 10 can be performed by using a distance Abetween the luminances of the respective circles 11 and 12, a distance Bbetween the luminances of the respective circles 11 and 13, and adistance C between the luminances of the respective circles 12 and 13.

Additionally, for example, in a case where one or both of the distancesA and C among the distances A to C are small, the detection of thesurvey marker 10 can be performed by using a distance between the huesof the respective circles 11 and 12, a distance between the hues of therespective circles 11 and 13, and a distance between the hues of therespective circles 12 and 13.

FIG. 34 is a flowchart for describing another example of the detectionprocessing of detecting the survey marker 10, which is performed by theCPU 32 of the cloud server 30 as the image processing apparatus of FIG.10.

In Step S131, the candidate region extraction unit 61 performs candidateregion extraction processing of extracting the candidate region from thecaptured image obtained from the camera 21.

In the candidate region extraction processing, in Step S131-1, thecandidate region extraction unit 61 binarizes (the pixel value of) eachpixel of the captured image to 1 or 0 depending on whether the pixel isa pixel of the circle 12 of the survey marker 10 or not.

The binarizing in Step S131-1 can be performed by, for example,threshold processing for the luminance or hue of the pixel.

In the candidate region extraction processing, in Step S131-2, thecandidate region extraction unit 61 performs erosion processing on abinarized image obtained by binarizing the captured image and controlsthe noise of the binarized image.

Furthermore, in the candidate region extraction processing, in StepS131-3, the candidate region extraction unit 61 performs dilationprocessing on the binarized image after the erosion processing.

After that, in the candidate region extraction processing, in StepS131-4, the candidate region extraction unit 61 performs outlinedetection processing of detecting the outline of the region of thepixels having the pixel values of, e.g., 1 in the binarized imageobtained after the dilation processing, that is, the outline of theregion of the pixels, in which it is assumed that the circle 12 appears,in the captured image.

In the candidate region extraction processing, in Step S131-5, thecandidate region extraction unit 61 then extracts a region correspondingto a minimum rectangle circumscribed to the outline detected by theoutline detection processing, as a candidate region, from the capturedimage, and supplies the region to the feature amount extraction unit 62.

In a case where there are a plurality of outlines detected by theoutline detection processing, a candidate region is extracted for eachof the plurality of outlines.

In Step S132, the feature amount extraction unit 62 performs featureamount extraction processing of extracting the feature amount of acandidate region for each of candidate regions obtained from thecandidate region extraction unit 61 and supplies the feature amount ofeach candidate region, which is obtained by the feature amountextraction processing, to the discrimination unit 63.

In the feature amount extraction processing in Step S132, the featureamount extraction unit 62 calculates, for example, the distance betweenthe luminances of the respective circles 11 and 12 in addition to thefeature amount as in the case of Step S32 in FIG. 11.

In other words, in the feature amount extraction processing in StepS132, the feature amount extraction unit 62 calculates the distancebetween the luminances of the respective circles 11 and 12, instead ofthe distance between the hues of the respective circles 11 and 12. In acase where the distance between the luminances is small, the featureamount extraction unit 62 calculates the distance between the hues ofthe respective circles 11 and 12.

Here, for example, in a case where the distance between the luminancesof respective FIGS. 11 and 12 is equal to or larger than a threshold ofthe distance between the luminances, which is similarly calculated forthe threshold TH of Expression (3), the candidate region is more likelyto be discriminated as the survey marker 10.

In Step S133, the discrimination unit 63 discriminates, for eachcandidate region, (the region showing) (the circle 12 of) the surveymarker 10 appearing in the captured image, in the captured image on thebasis of the feature amount of the candidate region from the featureamount extraction unit 62.

In other words, the discrimination unit 63 discriminates whether thecandidate region includes the survey marker 10 or not on the basis ofthe feature amount of the candidate region, as in the case of Step S33in FIG. 11.

Furthermore, in a case of discriminating that the candidate regionincludes the survey marker 10, the discrimination unit 63 detects thesurvey marker 10 from the captured image obtained from the camera 21 onthe basis of a discrimination result and outputs a detection result.

Note that in Step S133, in a case where the distance between therespective circles 11 and 12 is large (equal to or larger thanthreshold), the discrimination unit 63 can discriminate whether thecandidate region includes the survey marker 10 or not without using thedistance between the hues of the respective circles 11 and 12.

Further, in Step S133, in a case where the distance between theluminances of the respective circles 11 and 12 is small (not large), thediscrimination unit 63 can use the distance between the hues of therespective circles 11 and 12 in order to discriminate whether thecandidate region includes the survey marker 10 or not.

As described above, when the feature amount of the candidate regionincludes the distance between the hues or luminances of the respectivecircles 11 and 12, the survey marker 10 can be detected more accurately.

FIG. 35 is a flowchart for describing an example of detailed processingof binarizing each pixel of the captured image, which is performed inStep S131-1 of FIG. 34.

Note that, here, in order to facilitate discrimination from the colorsin nature, a chromatic color having both of the luminance L and the hueH (e.g., red color) is used for the color of the circle 12 of the surveymarker 10.

In Step S151, the candidate region extraction unit 61 selects one of thepixels of the captured image, which is not yet selected as a pixel ofinterest, as a pixel of interest, and the processing proceeds to StepS152.

In Step S152, the candidate region extraction unit 61 calculates andthen acquires the luminance L and the hue H of the pixel of interest,and the processing proceeds to Step S153.

In Step S153, the candidate region extraction unit 61 determines whetherthe hue H of the pixel of interest is considered as the hue of the colorof the circle 12 or not, that is, whether the hue H of the pixel ofinterest satisfies an expression of α<H and an expression of H<β or not.

Here, α and β represent the minimum value and the maximum value,respectively, in the range considered as the hue of the color of thecircle 12, and are set in advance.

Furthermore, in Step S153, the candidate region extraction unit 61determines whether the luminance L of the pixel of interest isconsidered as the luminance of the circle 12 or not, that is, whetherthe luminance L of the pixel of interest satisfies an expression of γ<Hand an expression of H<δ or not.

Here, γ and δ represent the minimum value and the maximum value,respectively, in the range considered as the luminance of the circle 12,and are set in advance.

In Step S153, in a case where the hue H of the pixel of interestsatisfies the expression of α<H and the expression of H<β, and theluminance L of the pixel of interest satisfies the expression of γ<H andthe expression of H<δ, the processing proceeds to Step S154.

In Step S154, the candidate region extraction unit 61 assumes that thepixel of interest is the pixel of the luminance and hue of the circle 12and sets the pixel value of the pixel of interest to 1 representing thepixel of the luminance and hue of the circle 12. The processing proceedsto Step S156.

Further, in Step S153, in a case where it is determined that the hue Hof the pixel of interest does not satisfy at least one of the expressionof α<H or the expression of H<β, or that the luminance L of the pixel ofinterest does not satisfy at least one of the expression of γ<H or theexpression of H<δ, the processing proceeds to Step S155.

In Step S155, the candidate region extraction unit 61 assumes that thepixel of interest is not the pixel of the luminance and hue of thecircle 12 and sets the pixel value of the pixel of interest to 0 notrepresenting the pixel of the luminance and hue of the circle 12. Theprocessing proceeds to Step S156.

In Step S156, the candidate region extraction unit 61 determines whetherall the pixels of the captured image are selected as the pixel ofinterest or not.

In Step S156, when it is determined that all the pixels of the capturedimage are not yet selected as the pixel of interest, the processingreturns to Step S151. In Step S151, the candidate region extraction unit61 newly selects one of the pixels of the captured image, which is notyet selected as the pixel of interest, as the pixel of interest, andsimilar processing is repeated thereafter.

Further, in Step S156, when it is determined that all the pixels of thecaptured image are each selected as the pixel of interest, thebinarizing processing is terminated.

FIG. 36 is a flowchart for describing an example of the processing ofextracting the distance between the luminances of the respective circles11 and 12 as a feature amount in the feature amount extractionprocessing performed in Step S132 of FIG. 34.

In Step S171, the feature amount extraction unit 62 assumes that thecandidate region is a region circumscribed to the circle 12, and detectsthe pixels showing (supposed to show) the respective circles 11 and 12existing in the candidate region (pixel in the region of the circle 11and pixel in the region of the circle 12). The processing proceeds toStep S172.

In Step S172, the feature amount extraction unit 62 calculates andacquires the luminance of each pixel in the region of the circle 11 andalso calculates and acquires the luminance of each pixel in the regionof the circle 12. The processing proceeds to Step S173.

In Step S173, the feature amount extraction unit 62 calculates, as thedistance between the luminances of the respective the circles 11 and 12,an absolute difference value between a mean value of the luminances ofthe respective pixels in the region of the circle 11 and a mean value ofthe luminances of the respective pixels in the region of the circle 12.The processing is then terminated.

As described above, the detection of the survey marker 10 from thecaptured image can be performed by using one or both of the distancesbetween the luminances and hues of the adjacent circles of the surveymarker 10.

Here, in this specification, the processing performed by the computersuch as the cloud server 30 according to a program is not necessarilyperformed in chronological order along the order described in theflowchart. In other words, the processing performed by the computeraccording to a program also includes processing executed in parallel orindividually (e.g., parallel processing or processing by object).

Further, the program may be processed by a single computer (processor)or may be distributed and processed by a plurality of computers.Furthermore, the program may be transferred to a remote computer to beexecuted.

Furthermore, in this specification, a system means an aggregation of aplurality of constituent elements (apparatus, module (parts), and thelike), regardless of whether all constituent elements are included inthe same casing or not. Therefore, a plurality of apparatusesaccommodated in separate casings and connected to one another via anetwork is a system, and one apparatus including a plurality of modulesin one casing is also a system.

Note that the embodiments of the present disclosure are not limited tothe embodiments described above and can be variously modified withoutdeparting from the gist of the present disclosure.

In other words, in this embodiment, the case where the presenttechnology is applied to the soil-volume measurement system has beendescribed, but the present technology can be applied to a system otherthan the soil-volume measurement system, for example, a system thatperforms arbitrary measurement for building or others by using aerialimage capturing of survey markers.

Furthermore, the present technology can have a configuration of cloudcomputing in which a plurality of apparatuses share one function andcooperate to perform processing via a network.

Further, the steps described in the flowchart described above can beexecuted by one apparatus or shared and executed by a plurality ofapparatuses.

Furthermore, in the case where one step includes a plurality ofprocessing steps, the plurality of processing steps in one step can beexecuted by one apparatus or shared and executed by a plurality ofapparatuses.

Further, the effects disclosed herein are merely exemplary ones and arenot restrictive ones, and any other effects may be produced.

Note that the present technology can have the following configurations.

<1> An image processing apparatus, including:

a candidate region extraction unit that extracts a candidate region froma captured image obtained by image capturing of a survey marker, thecandidate region being a candidate of a region in which the surveymarker appears, the survey marker having a planar shape and including aplurality of circles concentrically disposed, the plurality of circlesincluding adjacent circles each having a different luminance or hue;

-   -   a feature amount extraction unit that extracts a feature amount        of the candidate region; and    -   a discrimination unit that discriminates the survey marker on        the basis of the feature amount.

<2> The image processing apparatus according to <1>, in which

the candidate region extraction unit extracts the candidate region byusing at least the hue among the hue, a saturation, and a lightness of acircle having a second smallest radius among the plurality of circles.

<3> The image processing apparatus according to <1> or <2>, in which

the feature amount extraction unit extracts, as the feature amount, adistance between the luminance or hue of a circle having a smallestradius and the luminance or hue of a circle having a second smallestradius among the plurality of circles.

<4> The image processing apparatus according to any one of <1> to <3>,in which the feature amount extraction unit extracts, as the featureamount, a correlation between the candidate region and a rotation imageobtained by rotating the candidate region by a predetermined angle otherthan an integer multiple of 2π.

<5> The image processing apparatus according to any one of <1> to <4>,in which

the feature amount extraction unit

-   -   applies a filter, which emphasizes a color provided to the        circle, to the candidate region and a template image of the        survey marker, and    -   extracts, as the feature amount, a correlation between the        candidate region and the template image after the filter is        applied.

<6> The image processing apparatus according to any one of <1> to <5>,in which

the survey marker includes a built-in detection apparatus that acquiresinformation regarding the survey marker, and

the candidate region extraction unit extracts the candidate region byusing the information regarding the survey marker, the information beingdetected by the detection apparatus.

<7> The image processing apparatus according to any one of <1> to <6>,in which

the survey marker includes a built-in illuminance detection apparatusthat detects illuminance,

the feature amount extraction unit extracts, as the feature amount, adistance between the luminance or hue of a circle having a smallestradius and the luminance or hue of a circle having a second smallestradius, among the plurality of circles, and the discrimination unit

-   -   compares the distance with a predetermined threshold, and    -   sets, on the basis of a comparison result, the predetermined        threshold to be used for discriminating whether the candidate        region includes the survey marker or not, by using the        illuminance of the survey marker, the illuminance being detected        by the illuminance detection apparatus.

<8> The image processing apparatus according to any one of <1> to <7>,in which

a discrimination result of the survey marker is used to create athree-dimensional model.

<9> The image processing apparatus according to <8>, in which

the three-dimensional model is used to perform soil-volume measurement.

<10> An image processing method, including:

extracting a candidate region from a captured image obtained by imagecapturing of a survey marker, the candidate region being a candidate ofa region in which the survey marker appears, the survey marker having aplanar shape and including a plurality of circles concentricallydisposed, the plurality of circles including adjacent circles eachhaving a different luminance or hue;

extracting a feature amount of the candidate region; and

discriminating the survey marker on the basis of the feature amount.

<11> A program for causing a computer to function as:

a candidate region extraction unit that extracts a candidate region froma captured image obtained by image capturing of a survey marker, thecandidate region being a candidate of a region in which the surveymarker appears, the survey marker having a planar shape and including aplurality of circles concentrically disposed, the plurality of circlesincluding adjacent circles each having a different luminance or hue;

a feature amount extraction unit that extracts a feature amount of thecandidate region; and

a discrimination unit that discriminates the survey marker on the basisof the feature amount.

<12> A survey marker, having a planar shape and including a plurality ofcircles concentrically disposed and each having a different radius, theplurality of circles including adjacent circles each having a differentluminance or hue.

<13> The survey marker according to <12>, in which the survey markerincludes two circles as the plurality of circles.

<14> The survey marker according to <12>, in which the survey markerincludes three circles as the plurality of circles.

<15> The survey marker according to any one of <12> to <14>, in which

colors of two adjacent circles among the plurality of circles are twopredetermined colors in which a distance between luminances or hues ofthe two respective regions obtained from a captured image has apredetermined threshold or more, the captured image being obtained byimage capturing of a marker including two adjacent regions having thecolors of the two circles.

<16> The survey marker according to <15>, in which

the colors of a circle having a smallest radius and a circle having asecond smallest radius among the plurality of circles are the twopredetermined colors.

<17> The survey marker according to <16>, in which

a portion of the circle having the second smallest radius, the portionbeing obtained by removing the circle having the smallest radius fromthe circle having the second smallest radius, has an area substantially1.0 to 3.0 times an area of the circle having the smallest radius.

<18> The survey marker according to any one of <12> to <17>, in which

the survey marker has a planar shape and includes a plurality of circlesconcentrically disposed, and a rectangle including the plurality ofcircles is disposed.

<19> The survey marker according to any one of <12> to <18>, in which

the survey marker includes a built-in illuminance detection apparatusthat detects illuminance, in a portion of a circle having a smallestradius among the plurality of circles.

REFERENCE SIGNS LIST

-   10 survey marker-   11 circle (columnar member)-   12, 13 circle (circular member)-   14 frame region-   20 drone-   21 camera-   30 cloud server-   31 bus-   32 CPU-   33 ROM-   34 RAM-   35 hard disk-   36 output unit-   37 input unit-   38 communication unit-   39 drive-   40 input/output interface-   41 removable recording medium-   61 candidate region extraction unit-   62 feature amount extraction unit-   63 discrimination unit-   111 communication unit-   112 control unit-   113 drive control unit-   114 flight mechanism-   121 flight control apparatus-   201 to 203, 213, 223, 231, 250 member-   300 HLS color space

1. An image processing apparatus, comprising: a candidate regionextraction unit that extracts a candidate region from a captured imageobtained by image capturing of a survey marker, the candidate regionbeing a candidate of a region in which the survey marker appears, thesurvey marker having a planar shape and including a plurality of circlesconcentrically disposed, the plurality of circles including adjacentcircles each having a different luminance or hue; a feature amountextraction unit that extracts a feature amount of the candidate region;and a discrimination unit that discriminates the survey marker on abasis of the feature amount.
 2. The image processing apparatus accordingto claim 1, wherein the candidate region extraction unit extracts thecandidate region by using at least the hue among the hue, a saturation,and a lightness of a circle having a second smallest radius among theplurality of circles.
 3. The image processing apparatus according toclaim 1, wherein the feature amount extraction unit extracts, as thefeature amount, a distance between the luminance or hue of a circlehaving a smallest radius and the luminance or hue of a circle having asecond smallest radius among the plurality of circles.
 4. The imageprocessing apparatus according to claim 1, wherein the feature amountextraction unit extracts, as the feature amount, a correlation betweenthe candidate region and a rotation image obtained by rotating thecandidate region by a predetermined angle other than an integer multipleof 2π.
 5. The image processing apparatus according to claim 1, whereinthe feature amount extraction unit applies a filter, which emphasizes acolor provided to the circle, to the candidate region and a templateimage of the survey marker, and extracts, as the feature amount, acorrelation between the candidate region and the template image afterthe filter is applied.
 6. The image processing apparatus according toclaim 1, wherein the survey marker includes a built-in detectionapparatus that acquires information regarding the survey marker, and thecandidate region extraction unit extracts the candidate region by usingthe information regarding the survey marker, the information beingdetected by the detection apparatus.
 7. The image processing apparatusaccording to claim 1, wherein the survey marker includes a built-inilluminance detection apparatus that detects illuminance, the featureamount extraction unit extracts, as the feature amount, a distancebetween the luminance or hue of a circle having a smallest radius andthe luminance or hue of a circle having a second smallest radius, amongthe plurality of circles, and the discrimination unit compares thedistance with a predetermined threshold, and sets, on a basis of acomparison result, the predetermined threshold to be used fordiscriminating whether the candidate region includes the survey markeror not, by using the illuminance of the survey marker, the illuminancebeing detected by the illuminance detection apparatus.
 8. The imageprocessing apparatus according to claim 1, wherein a discriminationresult of the survey marker is used to create a three-dimensional model.9. The image processing apparatus according to claim 8, wherein thethree-dimensional model is used to perform soil-volume measurement. 10.An image processing method, comprising: extracting a candidate regionfrom a captured image obtained by image capturing of a survey marker,the candidate region being a candidate of a region in which the surveymarker appears, the survey marker having a planar shape and including aplurality of circles concentrically disposed, the plurality of circlesincluding adjacent circles each having a different luminance or hue;extracting a feature amount of the candidate region; and discriminatingthe survey marker on a basis of the feature amount.
 11. A program forcausing a computer to function as: a candidate region extraction unitthat extracts a candidate region from a captured image obtained by imagecapturing of a survey marker, the candidate region being a candidate ofa region in which the survey marker appears, the survey marker having aplanar shape and including a plurality of circles concentricallydisposed, the plurality of circles including adjacent circles eachhaving a different luminance or hue; a feature amount extraction unitthat extracts a feature amount of the candidate region; and adiscrimination unit that discriminates the survey marker on a basis ofthe feature amount.
 12. A survey marker, having a planar shape andincluding a plurality of circles concentrically disposed and each havinga different radius, the plurality of circles including adjacent circleseach having a different luminance or hue.
 13. The survey markeraccording to claim 12, wherein the survey marker includes two circles asthe plurality of circles.
 14. The survey marker according to claim 12,wherein the survey marker includes three circles as the plurality ofcircles.
 15. The survey marker according to claim 12, wherein colors oftwo adjacent circles among the plurality of circles are twopredetermined colors in which a distance between luminances or hues ofthe two respective regions obtained from a captured image has apredetermined threshold or more, the captured image being obtained byimage capturing of a marker including two adjacent regions having thecolors of the two circles.
 16. The survey marker according to claim 15,wherein the colors of a circle having a smallest radius and a circlehaving a second smallest radius among the plurality of circles are thetwo predetermined colors.
 17. The survey marker according to claim 16,wherein a portion of the circle having the second smallest radius, theportion being obtained by removing the circle having the smallest radiusfrom the circle having the second smallest radius, has an areasubstantially 1.0 to 3.0 times an area of the circle having the smallestradius.
 18. The survey marker according to claim 12, wherein the surveymarker has a planar shape and includes a plurality of circlesconcentrically disposed, and a rectangle including the plurality ofcircles is disposed.
 19. The survey marker according to claim 12,wherein the survey marker includes a built-in illuminance detectionapparatus that detects illuminance, in a portion of a circle having asmallest radius among the plurality of circles.